diff --git a/search-index.json b/search-index.json
index bd8241a..96a5ebf 100644
--- a/search-index.json
+++ b/search-index.json
@@ -1 +1 @@
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It provides EOSC users with recommendations concerning resources that could be of their interest, based on a multi-focal perspective of the users. Measuring the success of such system is crucial to get valuable insights in many aspects that affect user experience. In this approach, an independent metrics framework as a service is being introduced to support the evaluation and adaptation of recommendation mechanisms. The use of additional diagnostic metrics and visualizations offers deeper and sometimes surprising insights into a model’s performance. The evaluation is quantitatively being performed by processing information such as resources, user actions, ratings, and recommendations in order to measure the impact of the AI-enhanced services and user satisfaction as well as to incorporate this feedback and improve the services provided, via a user-friendly API and dashboard UI.","s":"Metrics Framework","u":"/eosc-recommender-metrics/docs/","h":"#metrics-framework","p":1},{"i":6,"t":"This module is responsible for: data retrieval through a connector module that claims and transforms data from various sources service-associated knowledge correlations dummy or dissociated data removal tagging of various associations in the data, i.e. registered or anonymous -related users and services generation of statistics information.","s":"Preprocessor","u":"/eosc-recommender-metrics/docs/","h":"#preprocessor","p":1},{"i":8,"t":"This module is responsible for: processing the data computing the designated evaluation metrics producing the necessary information in a homogenized manner.","s":"RS Metrics","u":"/eosc-recommender-metrics/docs/","h":"#rs-metrics","p":1},{"i":10,"t":"The RS Metrics software designs and exposes information graphs concerning attributes defined in the RS. The current implementation exposes: User Actions per day Recommended Items per day","s":"Graphs","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"","p":9},{"i":12,"t":"Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of user actions occurred. The granularity of the information is per day.","s":"User Actions per day","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"#user-actions-per-day","p":9},{"i":14,"t":"Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of recommended items delivered to the users. The granularity of the information is per day.","s":"Recommended Items per day","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"#recommended-items-per-day","p":9},{"i":16,"t":"The RS Metrics software implements a suite of KPIs exposed through a rich UI/dashboard and a rest API. KPIs indicate measurable values that demonstrate how effectively a company is achieving key business objectives. It currently delivers the below KPIs metrics: Click-Through Rate Hit Rate Top 5 ordered Services Top 5 recommended Services","s":"KPIs","u":"/eosc-recommender-metrics/docs/dashboard_views/kpis","h":"","p":15},{"i":18,"t":"The Recommender Metrics Framework is being developed under the umbrella of the EOSC-Future; an EU-funded H2020 project implementing the European Open Science Cloud (EOSC).","s":"About","u":"/eosc-recommender-metrics/docs/about","h":"","p":17},{"i":20,"t":"The Recommender Metrics Framework users can communicate for support through argo AT grnet DOT gr.","s":"Support","u":"/eosc-recommender-metrics/docs/about","h":"#support","p":17},{"i":22,"t":"The RS Metrics software implements a web service presenting reports through a rich UI/dashboard and a rest API. Complete documentation of how metrics perform the necessary calculations, and what is the expected range of the output values is also exhibited. It currently delivers the below metrics: Catalog Coverage Diversity Gini Index Diversity Shannon Entropy Novelty User Coverage","s":"RS Metrics","u":"/eosc-recommender-metrics/docs/dashboard_views/rsmetrics","h":"","p":21},{"i":24,"t":"Overview of the Recommender Metrics Framework Demonstration of the Recommender Metrics Framework in the EGI 2023 conference (19-23 June 2023, Poznan, Poland)","s":"Material","u":"/eosc-recommender-metrics/docs/material","h":"","p":23},{"i":27,"t":"Measures Recommendations' accuracy based on users' access to the services. A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#summary","p":25},{"i":29,"t":"The accuracy (A(A(A of the recommendations is based on users' access to the services. A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction. Generally, the Accuracy mathematical expression is defined as: A=Number of correct predictionsTotal number of predictionsA=\\frac{Number\\;of\\;correct\\;predictions}{Total\\;number\\;of\\;predictions}A=TotalnumberofpredictionsNumberofcorrectpredictions​ In RS Metrics the computation is determined by the following formula: Accuracy=Number of correctly recommended servicesTotal number of servicesAccuracy=\\frac{Number\\;of\\;correctly\\;recommended\\;services}{Total\\;number\\;of\\;services}Accuracy=TotalnumberofservicesNumberofcorrectlyrecommendedservices​where correctness is defined as if the service is both accessed by the user and also it is recommended by the RS","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#description","p":25},{"i":31,"t":"Type Float Min 0 Max 1 info A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#output","p":25},{"i":33,"t":"recommendations without anonymous users all available users (with their accessed services) all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#prerequisites","p":25},{"i":35,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" accordingly Vector creation of the Accessed Services​ For each user create a vector at the size of the number of the services, and assign a binary value for each service with a value of 1 if it is found in the user's accessed services, or 0 if it is not Vector creation of the Recommended Services​ For each user create a vector at the size of the number of the services, and assign a binary value for each service with a value of 1 if it is recommended to the user, or 0 if it is not Accuracy score computation​ For each user compute the average value of the difference vector; a vector which states True if service is found in both accessed and recommended vectors or False if it is not Mean value of Accuracy score​ Computation of the overall value by calculating the mean value of each user's accuracy score","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#process-flow","p":25},{"i":43,"t":"The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#summary","p":41},{"i":45,"t":"The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users. Takes into account all historical data of user actions. The metric is expressed by the formula: Click−ThroughRate=clicksviewsClick-Through Rate=\\frac{clicks}{views}Click−ThroughRate=viewsclicks​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#description","p":41},{"i":47,"t":"Type Float Min 0 Max +∞\\infty∞ info A value of 0 indicates that no clicks through recommendations panels occurred.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#output","p":41},{"i":49,"t":"all available user actions","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#prerequisites","p":41},{"i":51,"t":"Retrieve user actions with recommendation panel​ Get only the user actions that present a recommendation panel to the user in the source page. Those are actions with the following source paths: /services /services /services/c/{any category name} Count user actions with recommendation panel​ Count the items in the above list as they represent the times recommendations panels were presented to the users of the portal Filter list​ Narrow the above list into a new subset by selecting only user actions that originate from a recommendation panel. Those are actions that have the 'recommendation' string in the Action column Count user actions with clicks through recommendation panel​ Count the items in the subset as they represent the times users clicked through recommendations Calculate ratio​ Divide the items of the subset with the items of the first list to get the click-through rate","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#process-flow","p":41},{"i":53,"t":"/services /services /services/c/{any category name}","s":"Retrieve user actions with recommendation panel","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#retrieve-user-actions-with-recommendation-panel","p":41},{"i":60,"t":"The percentage (%) of the division of the unique services found in recommendations to the total number of published services.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#summary","p":58},{"i":62,"t":"The Catalog Coverage is described by the formula: unique_rec_servicesservices\\frac{unique\\_rec\\_services}{services}servicesunique_rec_services​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#description","p":58},{"i":64,"t":"Type Float Min 0 Max 100 info Catalog Coverage is 0 when none of the services is being recommended, and 100 when all of them are being recommended.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#output","p":58},{"i":66,"t":"all available recommendations all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#prerequisites","p":58},{"i":68,"t":"Retrieve recommendations​ Retrieve all available recommendations found in source Gather all unique services​ Gather all unique services found in all available recommendations Retrieve services​ Retrieve all available published services found in source Calculate ratio​ Calculate the percentage (%) of the division of the unique services found in recommendations to the total number of published services","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#process-flow","p":58},{"i":75,"t":"Measures Recommendations' diversity. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#summary","p":73},{"i":77,"t":"The diversity (GGG) of the recommendations according to Gini Index. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen (see book). Generally, the Gini Index mathematical expression is defined as: G=1n−1∑j=1n(2j−n−1)p(ij)G=\\frac{1}{n-1}\\sum_{j=1}^{n}(2j-n-1)p(i_j)G=n−11​∑j=1n​(2j−n−1)p(ij​), where i1,…,ini_1,\\ldots,i_ni1​,…,in​ is the list of items ordered according to increasing p(i)p(i)p(i) and each item iii accounts for a proportion p(i)p(i)p(i) of user recommendations. In RS Metrics the computation is determined by the following formula: Diversity=1n−1∑j=1n(2j−n−1)(count(j)recommendations)Diversity=\\frac{1}{n-1}\\sum_{j=1}^{n}(2j-n-1)\\left(\\frac{count(j)}{recommendations}\\right)Diversity=n−11​∑j=1n​(2j−n−1)(recommendationscount(j)​)","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#description","p":73},{"i":79,"t":"Type Float Min 0 Max 1 info The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#output","p":73},{"i":81,"t":"recommendations without anonymous users all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#prerequisites","p":73},{"i":83,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly Services Impact​ Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(j) Sort Services Impact from low to high​ Sort the number of how many times each service (i.e. i) was suggested from the lower to the higher value, in order to apply the respective weight (j). The computation includes services with 0 recommendation occurrence Recommended Probability of the Services​ For each service calculate its recommended probability by dividing the number of service's occurrence found in the recommendations to the total number of recommendations Service-based product computation​ Calculation of the product of the recommended probability from previous step and services' respective index j, for each service individually Gini Index computation​ Computation of the overall value by summing all values from previous step","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#process-flow","p":73},{"i":92,"t":"Measures Recommendations' diversity. The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#summary","p":90},{"i":94,"t":"The diversity (HHH) of the recommendations according to Shannon Entropy. The entropy is 0 when a single item is always chosen or recommended, and log(n) when n items are chosen or recommended equally often (see book). Generally, the Shannon Entropy mathematical expression is defined as: H=−∑i=1np(i)log⁡2p(i)H=-\\sum_{i=1}^{n}p(i)\\log_2 p(i)H=−∑i=1n​p(i)log2​p(i) In RS Metrics the formula is determined as: Diversity=−∑i=1services(count(i)recommendations)log⁡2(count(i)recommendations)Diversity=-\\sum_{i=1}^{services}\\left(\\frac{count(i)}{recommendations}\\right)\\log_2 \\left(\\frac{count(i)}{recommendations}\\right)Diversity=−∑i=1services​(recommendationscount(i)​)log2​(recommendationscount(i)​)","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#description","p":90},{"i":96,"t":"Type Float Min 0 Max +∞\\infty∞ info The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#output","p":90},{"i":98,"t":"recommendations without anonymous users all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#prerequisites","p":90},{"i":100,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly Services Impact​ Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(i) Recommended Probability of the Services​ For each service calculate its recommended probability by dividing the number of service's occurrences found in the recommendations to the total number of recommendations Service-based product computation​ Calculation of the product of the recommended probability from previous step and the logarithmic value of it, for each service individually Shannon Entropy computation​ Computation of the overall value by summing all values from previous step","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#process-flow","p":90},{"i":108,"t":"The ratio of user hits divided by the total number of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#summary","p":106},{"i":110,"t":"The ratio of user hits divided by the total number of users (user hit: a user that has accessed at least one service that is also a personal recommendation). The metric is expressed by the formula: HitRate=hitsusersHit Rate=\\frac{hits}{users}HitRate=usershits​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#description","p":106},{"i":112,"t":"Type Float Min 0 Max +∞\\infty∞ info A value of 0 indicates that no user hits occurred.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#output","p":106},{"i":114,"t":"all available recommendations by registered users all available users","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#prerequisites","p":106},{"i":116,"t":"Retrieve user-service association​ For each user get the recommended services and the services the user accessed \"services\" files accordingly Calculate hits​ Check if the user has at least one accessed service in recommendations. If yes increase number of hits by one Calculate ratio​ Divide user hits by the total number of users","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#process-flow","p":106},{"i":122,"t":"The novelty metric expresses the ability of the system to recommend items not generally seen before by the population of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#summary","p":120},{"i":124,"t":"Calculating novelty of the recommender system based on the user actions using the following formula: Novelty=∑i∈R−log(p(i))∣R∣Novelty=\\frac{\\sum\\nolimits_{i \\in R}-log(p(i))}{|R|}Novelty=∣R∣∑i∈R​−log(p(i))​ For each service item iii belonging to the set of recommended services RRR calculate the portion p(i)p(i)p(i) of the times the service has been viewed to the total views of the services produced by the user actions data.","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#description","p":120},{"i":126,"t":"Type Float Min 0 Max +∞\\infty∞ info Novelty expresses the ability of the system to recommend items that are novel (not seen before) by the population of users. A smaller number expresses that more services are being recommended that the users have not seen before.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#output","p":120},{"i":128,"t":"all available recommendations associated with registered users a subset of the available user actions associated with registered users that expresses transitions to service pages","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#prerequisites","p":120},{"i":130,"t":"Clean up​ Recommendations and user actions clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly User actions that target services​ Identify and keep user actions that express transition to target pages that are views of services. Additionally, user actions where the source and the target page belong to the same service's space are removed from the process. Calculate views for each service​ Group and count user actions that express views for each recommended service id Calculate view propability p(i) of each service​ Calculation of the view propability of each service which is the fraction of the service's views to the total service views Overall Novelty computation​ Computation of the overall value by summing the negative log of all recommended service views from previous step and dividing them by the total number of recommended services","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#process-flow","p":120},{"i":138,"t":"The percentage (%) of the division of the unique users found in recommendations to the total number of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#summary","p":136},{"i":140,"t":"The User Coverage is described by the formula unique_rec_usersusers\\frac{unique\\_rec\\_users}{users}usersunique_rec_users​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#description","p":136},{"i":142,"t":"Type Float Min 0 Max 100 info User Coverage is 0 when recommendations are being suggested to none users, and 100 when recommendations are being suggested to all of the users.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#output","p":136},{"i":144,"t":"all available recommendations all available users","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#prerequisites","p":136},{"i":146,"t":"Retrieve recommendations​ Retrieve all available recommendations found in source Gather all unique users​ Gather all unique users found in all available recommendations Retrieve users​ Retrieve all available users found in source Calculate ratio​ Calculate the percentage (%) of the division of the unique users found in recommendations to the total number of users","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#process-flow","p":136},{"i":152,"t":"Controller details Α public limited company (societe anonyme) under the corporate name \"National Infrastructures for Technology and Research S.A.\" and the distinctive title \"GRNET S.A.\" Controller's Contact Details dpo at grnet.gr Processor Details ARGO Support Team Processor's Contact Details argo at grnet.gr Scope of this Privacy Statement National Infrastructures for Technology and Research S.A. (hereinafter referred to as \"GRNET SA\") is bound by European Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation -- hereinafter referred to as \"the GDPR\") and Law 4624/2019 (Government Gazette 137/A/2019) on \"Data Protection Authority, measures for the implementation of Regulation(EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and for the incorporation into national law of Directive(EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 and other provisions\", as in force at any time (hereinafter referred to as \"the Law\"). This Privacy Statement details all information necessary for the processing of personal data carried out in the context of Argo Service Monitoring., as well as the policies and procedures implemented by GRNET SA for the protection of the EOSC Recommender Service - Metrics service users privacy. This Privacy Statement sets out the criteria as well as the terms and conditions under which GRNET SA collects, processes, uses, stores and transmits the personal data of the service users, how it ensures the confidentiality of such information, including any law and/or regulation implemented or enacted in accordance with Union and national laws on personal data protection and electronic privacy, as well as any law and/or regulation amending, replacing, issuing or consolidating any of the latter, including any other applicable Union and national laws on the processing of personal data and privacy, which may exist in accordance with applicable law. GRNET SA reserves the right to amend and update this Privacy Statement whenever necessary, whereas any such updates shall become effective five (5) days after they have been posted on the EOSC Recommender Service - Metrics service website. For the purposes of this Privacy Statement, the terms \"processor\", \"controller\", \"third party\", \"supervising authority\", \"personal data\", \"processing\", \"data subject\" shall have the meaning ascribed to them by applicable legislation on the protection of personal data. In addition, for the purposes of the present, the following definitions shall also apply: \"Website\" -- the website accessible via domain name web-ui-url including the entirety of the web pages thereof. \" EOSC Recommender Service - Metrics \" - web-ui-url \"User\"- the EOSC Recommender Service - Metrics online service user. The acces is restricted to specific user. The dentity is unknown or may not be verified.","s":"Privacy Policy","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"","p":151},{"i":154,"t":"i. GRNET SA-- as processor -- processes its \"users\" personal data as referred to in the following section, for the following purposes: Providing the EOSC Recommender Service - Metrics service Technical support to the \"coordinators\" of the EOSC Recommender Service - Metrics service Easy and user friendly operation of the \"EOSC Recommender Service - Metrics service\". Proof of Acceptance of the Terms of Use and the Privacy Statement Creation of statistical reports and charts to monitor the EOSC Recommender Service - Metrics service service Statistical reports and charts data do not contain any \"users\" personal data, as they result from anonymized information. ii. GRNET S.A. -- as the processor -- collects and processes personal data of \"users\" using its infrastructure for the purpose of providing the EOSC Recommender Service - Metrics service. iii. Special categories of personal data GRNET SA does not collect, process or gain access in any way to specific data categories, as set forth in the provisions of the legislation in force (in particular data relating to racial or ethnic origin, religion, health data, etc.). In the event that a \"user\" posts any such special category data on the \"website\" or on the Argo Service Monitoring, such data will be removed as soon as the management team become aware thereof.","s":"A. Purpose/s for processing the data collected:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#a-purposes-for-processing-the-data-collected","p":151},{"i":156,"t":"The processing of \"users\" personal data is necessary for the performance of the Agreement on the provision of EOSC Recommender Service - Metrics service, in accordance with the needs (technical and organisational) to provide the best possible services, to serve the \"users\".","s":"B. Legal bases for processing","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#b-legal-bases-for-processing","p":151},{"i":158,"t":"For providing EOSC Recommender Service - Metrics service and the seamless operation of such services, access to the \"users\" personal data shall be granted to the following: To the EOSC Recommender Service - Metrics service support team, consisting of personnel engaged in a contractual relationship of either a project or an independent service agreement with GRNET SA. hereinafter referred to as \"GRNET associates\". The processing of EOSC Recommender Service - Metrics service \"users\" personal data by the aforementioned, is carried out under the supervision and only at the request of GRNET SA, within the scope of the mission and the role of each associate. Such associates undertake to comply with the same privacy and personal data requirements as GRNET SA itself in accordance to the present Privacy Statement","s":"C. Access to personal data:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#c-access-to-personal-data","p":151},{"i":160,"t":"GRNET SA shall in no way transmit or in any way disclose the EOSC Recommender Service - Metrics service \"users\" personal data to any third-party entities, private businesses, natural persons or legal entities, public authorities, agencies or other organizations, other than as expressly set out herein. The Argo Service Monitoring service \"users\" personal data may be disclosed or transmitted to governmental authorities and/or law enforcement officials, only if necessary for the abovementioned purposes, in the context of enforcement of a court decision or a provision of law or if necessary to secure the legitimate interests of GRNET SA in its capacity as processor, in compliance with the terms and conditions of applicable law.","s":"D. Recipients of collected personal data:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#d-recipients-of-collected-personal-data","p":151},{"i":162,"t":"The EOSC Recommender Service - Metrics service users personal data shall be retained no longer than it is necessary for the needs of the service and the audits the service is subjected to. More specifically: Categories of personal data collected Time and place of retention of personal data IP address Data from website navigation through Cookies 18 months (log retention)","s":"E. Personal data retention periods","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#e-personal-data-retention-periods","p":151},{"i":164,"t":"The processing of personal data by GRNET SA is performed in a manner that ensures both confidentiality and security thereof. All appropriate organisational and technical measures shall be taken to safeguard data against any accidental or unlawful destruction, accidental loss, alteration, prohibited dissemination or access or any other form of unfair processing. The services provided by GRNET SA are constantly evaluated to be in line with the safety requirements of international standards. GRNET's Information Security Management System (ISMS) has been certified by the accredited certification body, EUROCERT SA In particular: Access to technical log data is restricted and can only be accessed in a secure way by the EOSC Recommender Service - Metrics service staff. When accessing the EOSC Recommender Service - Metrics servicee adequate security controls are in place to keep your personal data safe in accordance with the classification of the personal data we have collected from you. We use encryption (HTTPS) to keep data private while in transit. Data sent using HTTPS is secured via Transport Layer Security protocol (TLS), which provides: Encryption---encrypting the exchanged data to keep it secure from droppers. Data integrity---data cannot be modified or corrupted during transfer, intentionally or otherwise, without being detected. Authentication---proves that your users communicate with the intended website. The implementation of the EOSC Recommender Service - Metrics service ensures that no unauthorized user can log into the service. An authorised user means a service user, who has an active account with the Federated AAI service, having passed the authentication process mentioned above. We review our information collection, storage, and processing practices, including physical security measures, to prevent unauthorized access to our systems Although we follow best security practices to ensure your personal data remains secure, there is no absolute guarantee of security when using services online. While we strive to protect your personal data, you acknowledge that: There are security and privacy limitations on the internet which are beyond our control and can have a negative impact on the confidentiality, integrity and availability of the information. We cannot be held accountable for activity that results from your own neglect to safeguard the security of your login credentials and equipment which results in a loss of your personal data. If you feel this not enough, then please do not provide any personal data. Your personal data will be protected according to the Code of Conduct for Service Providers , a common standard for the research and higher education sector to protect your privacy.","s":"F. Privacy and Security of Information:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#f-privacy-and-security-of-information","p":151},{"i":166,"t":"For any questions or clarifications regarding the present Privacy Statement and as well as in the event of any violation related to personal data issues, \"users\" may contact the Competent Department of GRNET SA at the e-mail address mentioned hereinabove. They may also contact the Data Protection Officer (DPO) of GRNET S.A., Ms. Vera Meleti, and/or the deputy DPO, Ms. Vasiliki Konstantinopoulou at the e-mail address: dpo@grnet.gr.","s":"G. Contact:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#g-contact","p":151},{"i":168,"t":"In the event that any EOSC Recommender Service - Metrics service \"user\" request is not satisfied by the processor, the \"user\" may at any time address to/ file recourse with the Competent Supervisory Authority, namely the Data Protection Authority https://www.dpa.gr .","s":"H. Recourse/Complaint:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#h-recoursecomplaint","p":151},{"i":170,"t":"By registering as a user you declare that you have read, understood and will abide by the following conditions of use: You shall only use the resources/services to perform work, or transmit or store data consistent with the stated goals, policies and conditions of use as defined by the body or bodies granting you access. You shall provide appropriate acknowledgement of support or citation for your use of the resources/services provided as required by the body or bodies granting you access. You shall not use the resources/services for any purpose that is unlawful and not (attempt to) breach or circumvent any administrative or security controls. You shall respect intellectual property and confidentiality agreements. You shall protect your access credentials (e.g. private keys, tokens or passwords). You shall keep all your registered information correct and up to date. You shall immediately report any known or suspected security breach or misuse of the resources/services or access credentials to the specified incident reporting locations and to the relevant credential issuing authorities. You use the resources/services at your own risk. There is no guarantee that the resources/services will be available at any time or that their integrity or confidentiality will be preserved or that they will suit any purpose. You agree that logged information, including personal data provided by you for registration purposes, may be used for administrative, operational, accounting, monitoring and security purposes. You agree that this logged information may be disclosed to other authorised participants via secured mechanisms, only for the same purposes and only as far as necessary to provide the services. You agree that the body or bodies granting you access and resource/service providers are entitled to regulate, suspend or terminate your access without prior notice and without compensation, within their domain of authority, and you shall immediately comply with their instructions. You are liable for the consequences of your violation of any of these conditions of use, which may include but are not limited to the reporting of your violation to your home institute and, if the activities are thought to be illegal, to appropriate law enforcement agencies.","s":"Terms of Use","u":"/eosc-recommender-metrics/docs/policies/terms","h":"","p":169},{"i":173,"t":"The Recommender Metrics Framework currently monitors the Recommender System of the EOSC Marketplace/Search Service by reporting diagnostic metrics and visualizations. The Recommender System (RS) is a component of the EOSC Search Service designed to enhance the user experience by providing recommendations based on a multi-focal perspective of the users. The software calculates statistics, metrics, KPIs, and visualizations to measure the success of the RS that offer deeper insights into the performance of the system. The evaluation process involves quantitatively processing information such as resources, user actions, ratings, and recommendations to measure the impact of the AI-enhanced services and user satisfaction. The feedback obtained from the evaluation is incorporated to improve the services provided through a user-friendly API and dashboard UI.","s":"Monitoring the Recommender System of the EOSC Marketplace/Search Service","u":"/eosc-recommender-metrics/docs/use_cases","h":"#monitoring-the-recommender-system-of-the-eosc-marketplacesearch-service","p":171},{"i":175,"t":"The Recommender Metrics Framework due to its design can be used as an analysis tool of the recommendation engine of a Recommender System. Specifically, if the RMF is fed with the necessary input information such as resources, user actions, ratings, and recommendations can carry out the preprocessing stage which is retrieving data from multiple sources through a connector module that claims and transforms the data, establishing connections between service-related knowledge, eliminating dummy or irrelevant data, tagging associations in the data such as registered or anonymous users and services, and generating statistical information. Subsequently, RMF will perform the next phase's steps which are: processing the data, computing the designated evaluation metrics, and uniformly presenting the resulting information. The results such as statistics, metrics, and KPIs will be displayed through a visually appealing UI/dashboard and a REST API, with comprehensive documentation on how metrics are calculated and the expected output range.","s":"Evaluate a third-party RS","u":"/eosc-recommender-metrics/docs/use_cases","h":"#evaluate-a-third-party-rs","p":171},{"i":177,"t":"This document describes the technical and organisational measures established by National Infrastructures for Research and Technology S.A. (GRNET S.A.) to meet legal and contractual requirements when processing personal data, conducting a higher level of security and protection. The definitions in Article 32 of the GDPR apply.","s":"Technical and organisational measures (TOM)","u":"/eosc-recommender-metrics/docs/policies/tom","h":"","p":176},{"i":180,"t":"Actions suitable for providing physical and environmental security of data centers, server room facilities, and working areas are adapted, while precautions against environmental threats and power disruptions are also granted. Access is limited by job role and subject to authorised approval. Technical and organisational measures that are taken into account involve security guard personnel, reception, doorbell systems, manual locking systems, and video surveillance of entrances. Entrance to the building is only possible with keycards and keys provided to authorised employees or by visitors accompanied by employees, preventing unauthorised persons from accessing secured areas. Additionally, Information on security policy, work instructions for operational safety, and access control are provided.","s":"1.a. Physical Access Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1a-physical-access-control","p":176},{"i":182,"t":"Operations sufficient for preventing data processing systems from being used by unauthorised persons are applied. Logical access controls are designed based on authority levels and job functions. Granting access is gained on a need-to-know and least privilege basis, where it is restricted to authorised employees responsible for the job. The use of unique IDs -identified through Active Directory- and passwords for all users is adapted, including a periodic review and revoking access when employment terminates or changes in job functions occur. Technical and organisational measures that are taken into account involve; username and password protected systems, intrusion detection facilities, use of Virtual Private Networks (VPNs) for remote access, firewalls, intrusion Detection System (IDS), user permission management, information security policy, work instruction of IT user regulations, operation security and access control.","s":"1.b. Logical Access Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1b-logical-access-control","p":176},{"i":184,"t":"Actions to ensure that those authorized to use a data processing system can only access the data subject to their access authorization -based on their rule- and that personal data cannot be read, copied, modified, or removed without authorization during processing, use and after storage. Technical operations incorporate physical deletion of data carriers, logging of accesses to applications, specifically when entering, changing, and deleting data, SSH encrypted access, and certified SSL encryption. At the organisational level, a minimum number of administrators is applied, management of user rights are controlled by administrators, work instruction communication security and handling of information and values are also claimed.","s":"1.c. Authorization Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1c-authorization-control","p":176},{"i":186,"t":"It is ensured that personal data collected for different purposes can be processed separately. Multi-tenancy of relevant applications is performed, or systems are physically or logically separated. The development sheet is separated for each product, and the services have their own line of environments. All environments, documents and other data are shared for the members of that project/product, while operational, information and data protection securities and policies are applied.","s":"1.d. Separation Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1d-separation-control","p":176},{"i":188,"t":"Operations for pseudonymization or anonymization of personal data are implemented to the extent necessary. Internal instruction to pseudonymize or anonymize personal data as far as possible in the event of disclosure or even after the statutory deletion period has expired. Specific internal regulations on cryptography, while operational, information and data protection securities and policies are applied.","s":"1.e. Pseudonymization","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1e-pseudonymization","p":176},{"i":191,"t":"Measures are taken into account to ensure that personal data cannot be read, copied, altered or removed by unauthorized persons during electronic transmission or while being transported or stored on data media. As technical and organizational actions are the use of Virtual Private Networks (VPNs) and firewalls, the provision via encrypted connections and techniques such as SSH, SFTP, HTTPS and secure cloudstores, the logging of accesses and retrievals, while operational, information and data protection securities and policies are also applied.","s":"2.a. Transfer Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#2a-transfer-control","p":176},{"i":193,"t":"Operations that ensure that it is possible to check and establish retrospectively whether and by whom personal data has been entered into, modified or removed from data processing systems are implemented to the extent necessary. Input control is achieved through logging, which can take place at various levels (e.g., operating system, network, firewall, database, application). Traceability of data entry, modification and deletion through individual user names, assignment of rights to enter, change and delete data on the basis of an authorisation concept, while information security policy and work instruction of IT user regulations are engaged.","s":"2.b. Input Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#2b-input-control","p":176},{"i":196,"t":"Actions to ensure that personal data is protected against accidental destruction or loss have been implemented to the required extent. Measures comprise fire and smoke detection systems, fire extinguishers, air-conditioning, temperature and humidity monitoring and video surveillance in server rooms, UPS system and emergency diesel generators deployment, RAID system and hard disk mirroring for data backup, information security policy and work instruction operational security.","s":"3.a. Availability Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#3a-availability-control","p":176},{"i":198,"t":"Data backups of databases and operating system images are taken to the extent required and with the aim of preventing the loss of personal data in the event of physical or technical incident. Backups are performed for network drives and servers in productive operation, where the process is being recorded (logged). The backup concept is applied according to criticality and customer specifications. When applicable storage of backup media obtained in a safe place outside the server room. Information security policy and work instruction of IT user regulations are also engaged.","s":"3.b. Recoverability Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#3b-recoverability-control","p":176},{"i":201,"t":"Technical and organizational measures that are taken into account are; central documentation of all data protection regulations with access for employees, security certification according to ISO 27001. Updates and reviews of the effectiveness of the TOMs are carried out periodically. Data protection checkpoints are consistently implemented, while data processing systems (IT systems) are checked regularly to the extent required and after changes to ensure that they are functioning properly. A Data Protection Officer (DPO) group is appointed and notified of physical or technical incidents, and staff is trained and obliged to confidentiality and data secrecy. Data Protection Impact Assessment (DPIA) is carried out as required, whereas processes regarding information obligations according to Art. 13 and 14 GDPR are established.","s":"4.a. Data Protection Management","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4a-data-protection-management","p":176},{"i":203,"t":"Technical and organizational actions have been established to the extent required for security breach response and data breach process. The use and the regular updating of firewall, spam filters, virus scanning, Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) for customer systems on order, are served. The process for detecting and reporting security incidents and data breaches is being documented via ticket system, with regard to reporting obligation to the supervisory authority. Formalized procedure for handling security incidents, including the involvement of DPO and ISO in security incidents and data breaches is available, while operational, information, data protection and IT user regulations, securities and policies are also applied.","s":"4.b Incident Response Management","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4b-incident-response-management","p":176},{"i":205,"t":"Measures pursuant to Art 25 GDPR comply with the principles of data protection by design and by default. No more personal data is collected than is necessary for the respective purpose. Data Protection Policy (includes principles \"privacy by design and by default\").","s":"4.c Data Protection by Design and by Default","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4c-data-protection-by-design-and-by-default","p":176},{"i":207,"t":"Actions to ensure that personal data processed on behalf of the client can only be processed in accordance with the client's instructions. Technical and organizational measures have been established to the required extent. Measures involve monitoring of remote access by external parties, in the context of remote support and work instruction supplier management and supplier evaluation. Moreover, a prior review of the security measures taken by the contractor and their documentation is applied. Selection of the contractor under due diligence aspects (especially with regard to data protection and data security) is achieved. Conclusion of the necessary data processing agreement on commissioned processing or EU standard contractual clauses and a framework agreement on contractual data processing within the group of companies, where written instructions to the contractor and obligation of the contractor's employees to maintain data secrecy. Additionally, an agreement on effective control rights over the contractor and regulations on the use of further subcontractors is maintained, ensuring also the destruction of data after termination of the contract or in the case of longer collaboration, ongoing review of the contractor and its level of protection.","s":"4.d Order Control (outsourcing, subcontractors, and order processing)","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4d-order-control-outsourcing-subcontractors-and-order-processing","p":176},{"i":209,"t":"The National Infrastructures for Research and Technology S.A. (GRNET S.A.), based on Its Quality and Information Security Policies has set itself the goal of providing products and services to be delivered at the highest possible level of information security in compliance with the law. In this context GRNET S.A. has established, the roles of Information Security Officer (ISO), Data Protection Officer (DPO), Quality Officer (QO), and Legal Compliance Officer (LCO) as well as a Corporate Binding Rules (a set of internal guidelines and regulations) on information security and data protection, that are contractually binding for all employees, that defines secure information and data handling formed in secrecy and confidentiality. Employees are continuously informed and trained in the area of data protection, while third parties who may come into contact with personal data in the course of their work for GRNET S.A. are obligated to comply with data protection and data secrecy by means of a so-called NDA (Non-Disclosure Agreement) before they begin their work. Any subcontractors entrusted with further processing are only used after approval by the clients and after the conclusion of a Data Processing Agreement (DPA) in accordance with Art 28 GDPR, with which they are fully bound by all data protection obligations to which GRNET S.A. itself is subject. Current high technical security standards at GRNET S.A. are periodically reviewed and confirmed for adequacy and effectiveness in the course of ongoing internal audits and annually by Independent, External, Accredited Certification Bodies.","s":"5. Organization and Data Protection","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#5-organization-and-data-protection","p":176},{"i":211,"t":"The Quality Management System (QMS) as well as the Information Security Management System (ISMS) of GRNET S.A. are both certified by Independent Accredited Certification Bodies according to ISO 9001 and ISO 27001.","s":"6. 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It provides EOSC users with recommendations concerning resources that could be of their interest, based on a multi-focal perspective of the users. Measuring the success of such system is crucial to get valuable insights in many aspects that affect user experience. In this approach, an independent metrics framework as a service is being introduced to support the evaluation and adaptation of recommendation mechanisms. The use of additional diagnostic metrics and visualizations offers deeper and sometimes surprising insights into a model’s performance. The evaluation is quantitatively being performed by processing information such as resources, user actions, ratings, and recommendations in order to measure the impact of the AI-enhanced services and user satisfaction as well as to incorporate this feedback and improve the services provided, via a user-friendly API and dashboard UI.","s":"Metrics Framework","u":"/eosc-recommender-metrics/docs/","h":"#metrics-framework","p":1},{"i":6,"t":"This module is responsible for: data retrieval through a connector module that claims and transforms data from various sources service-associated knowledge correlations dummy or dissociated data removal tagging of various associations in the data, i.e. registered or anonymous -related users and services generation of statistics information.","s":"Preprocessor","u":"/eosc-recommender-metrics/docs/","h":"#preprocessor","p":1},{"i":8,"t":"This module is responsible for: processing the data computing the designated evaluation metrics producing the necessary information in a homogenized manner.","s":"RS Metrics","u":"/eosc-recommender-metrics/docs/","h":"#rs-metrics","p":1},{"i":10,"t":"The Recommender Metrics Framework is being developed under the umbrella of the EOSC-Future; an EU-funded H2020 project implementing the European Open Science Cloud (EOSC).","s":"About","u":"/eosc-recommender-metrics/docs/about","h":"","p":9},{"i":12,"t":"The Recommender Metrics Framework users can communicate for support through argo AT grnet DOT gr.","s":"Support","u":"/eosc-recommender-metrics/docs/about","h":"#support","p":9},{"i":14,"t":"The RS Metrics software designs and exposes information graphs concerning attributes defined in the RS. The current implementation exposes: User Actions per day Recommended Items per day","s":"Graphs","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"","p":13},{"i":16,"t":"Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of user actions occurred. The granularity of the information is per day.","s":"User Actions per day","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"#user-actions-per-day","p":13},{"i":18,"t":"Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of recommended items delivered to the users. The granularity of the information is per day.","s":"Recommended Items per day","u":"/eosc-recommender-metrics/docs/dashboard_views/graphs","h":"#recommended-items-per-day","p":13},{"i":20,"t":"The RS Metrics software implements a suite of KPIs exposed through a rich UI/dashboard and a rest API. KPIs indicate measurable values that demonstrate how effectively a company is achieving key business objectives. It currently delivers the below KPIs metrics: Click-Through Rate Hit Rate Top 5 ordered Services Top 5 recommended Services","s":"KPIs","u":"/eosc-recommender-metrics/docs/dashboard_views/kpis","h":"","p":19},{"i":22,"t":"Overview of the Recommender Metrics Framework Demonstration of the Recommender Metrics Framework in the EGI 2023 conference (19-23 June 2023, Poznan, Poland)","s":"Material","u":"/eosc-recommender-metrics/docs/material","h":"","p":21},{"i":25,"t":"The percentage (%) of the division of the unique services found in recommendations to the total number of published services.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#summary","p":23},{"i":27,"t":"The Catalog Coverage is described by the formula: unique_rec_servicesservices\\frac{unique\\_rec\\_services}{services}servicesunique_rec_services​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#description","p":23},{"i":29,"t":"Type Float Min 0 Max 100 info Catalog Coverage is 0 when none of the services is being recommended, and 100 when all of them are being recommended.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#output","p":23},{"i":31,"t":"all available recommendations all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#prerequisites","p":23},{"i":33,"t":"Retrieve recommendations​ Retrieve all available recommendations found in source Gather all unique services​ Gather all unique services found in all available recommendations Retrieve services​ Retrieve all available published services found in source Calculate ratio​ Calculate the percentage (%) of the division of the unique services found in recommendations to the total number of published services","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/catalog_coverage","h":"#process-flow","p":23},{"i":40,"t":"Measures Recommendations' accuracy based on users' access to the services. A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#summary","p":38},{"i":42,"t":"The accuracy (A(A(A of the recommendations is based on users' access to the services. A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction. Generally, the Accuracy mathematical expression is defined as: A=Number of correct predictionsTotal number of predictionsA=\\frac{Number\\;of\\;correct\\;predictions}{Total\\;number\\;of\\;predictions}A=TotalnumberofpredictionsNumberofcorrectpredictions​ In RS Metrics the computation is determined by the following formula: Accuracy=Number of correctly recommended servicesTotal number of servicesAccuracy=\\frac{Number\\;of\\;correctly\\;recommended\\;services}{Total\\;number\\;of\\;services}Accuracy=TotalnumberofservicesNumberofcorrectlyrecommendedservices​where correctness is defined as if the service is both accessed by the user and also it is recommended by the RS","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#description","p":38},{"i":44,"t":"Type Float Min 0 Max 1 info A value of 1, indicates that the RS model got all the predictions right, and a value of 0 indicates that the RS model did not make a single correct prediction.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#output","p":38},{"i":46,"t":"recommendations without anonymous users all available users (with their accessed services) all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#prerequisites","p":38},{"i":48,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" accordingly Vector creation of the Accessed Services​ For each user create a vector at the size of the number of the services, and assign a binary value for each service with a value of 1 if it is found in the user's accessed services, or 0 if it is not Vector creation of the Recommended Services​ For each user create a vector at the size of the number of the services, and assign a binary value for each service with a value of 1 if it is recommended to the user, or 0 if it is not Accuracy score computation​ For each user compute the average value of the difference vector; a vector which states True if service is found in both accessed and recommended vectors or False if it is not Mean value of Accuracy score​ Computation of the overall value by calculating the mean value of each user's accuracy score","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/accuracy","h":"#process-flow","p":38},{"i":55,"t":"The RS Metrics software implements a web service presenting reports through a rich UI/dashboard and a rest API. Complete documentation of how metrics perform the necessary calculations, and what is the expected range of the output values is also exhibited. It currently delivers the below metrics: Catalog Coverage Diversity Gini Index Diversity Shannon Entropy Novelty User Coverage","s":"RS Metrics","u":"/eosc-recommender-metrics/docs/dashboard_views/rsmetrics","h":"","p":54},{"i":58,"t":"Measures Recommendations' diversity. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#summary","p":56},{"i":60,"t":"The diversity (GGG) of the recommendations according to Gini Index. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen (see book). Generally, the Gini Index mathematical expression is defined as: G=1n−1∑j=1n(2j−n−1)p(ij)G=\\frac{1}{n-1}\\sum_{j=1}^{n}(2j-n-1)p(i_j)G=n−11​∑j=1n​(2j−n−1)p(ij​), where i1,…,ini_1,\\ldots,i_ni1​,…,in​ is the list of items ordered according to increasing p(i)p(i)p(i) and each item iii accounts for a proportion p(i)p(i)p(i) of user recommendations. In RS Metrics the computation is determined by the following formula: Diversity=1n−1∑j=1n(2j−n−1)(count(j)recommendations)Diversity=\\frac{1}{n-1}\\sum_{j=1}^{n}(2j-n-1)\\left(\\frac{count(j)}{recommendations}\\right)Diversity=n−11​∑j=1n​(2j−n−1)(recommendationscount(j)​)","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#description","p":56},{"i":62,"t":"Type Float Min 0 Max 1 info The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#output","p":56},{"i":64,"t":"recommendations without anonymous users all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#prerequisites","p":56},{"i":66,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly Services Impact​ Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(j) Sort Services Impact from low to high​ Sort the number of how many times each service (i.e. i) was suggested from the lower to the higher value, in order to apply the respective weight (j). The computation includes services with 0 recommendation occurrence Recommended Probability of the Services​ For each service calculate its recommended probability by dividing the number of service's occurrence found in the recommendations to the total number of recommendations Service-based product computation​ Calculation of the product of the recommended probability from previous step and services' respective index j, for each service individually Gini Index computation​ Computation of the overall value by summing all values from previous step","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_gini_index","h":"#process-flow","p":56},{"i":75,"t":"The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#summary","p":73},{"i":77,"t":"The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users. Takes into account all historical data of user actions. The metric is expressed by the formula: Click−ThroughRate=clicksviewsClick-Through Rate=\\frac{clicks}{views}Click−ThroughRate=viewsclicks​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#description","p":73},{"i":79,"t":"Type Float Min 0 Max +∞\\infty∞ info A value of 0 indicates that no clicks through recommendations panels occurred.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#output","p":73},{"i":81,"t":"all available user actions","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#prerequisites","p":73},{"i":83,"t":"Retrieve user actions with recommendation panel​ Get only the user actions that present a recommendation panel to the user in the source page. Those are actions with the following source paths: /services /services /services/c/{any category name} Count user actions with recommendation panel​ Count the items in the above list as they represent the times recommendations panels were presented to the users of the portal Filter list​ Narrow the above list into a new subset by selecting only user actions that originate from a recommendation panel. Those are actions that have the 'recommendation' string in the Action column Count user actions with clicks through recommendation panel​ Count the items in the subset as they represent the times users clicked through recommendations Calculate ratio​ Divide the items of the subset with the items of the first list to get the click-through rate","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#process-flow","p":73},{"i":85,"t":"/services /services /services/c/{any category name}","s":"Retrieve user actions with recommendation panel","u":"/eosc-recommender-metrics/docs/metrics_descriptions/click_through_rate","h":"#retrieve-user-actions-with-recommendation-panel","p":73},{"i":92,"t":"Measures Recommendations' diversity. The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#summary","p":90},{"i":94,"t":"The diversity (HHH) of the recommendations according to Shannon Entropy. The entropy is 0 when a single item is always chosen or recommended, and log(n) when n items are chosen or recommended equally often (see book). Generally, the Shannon Entropy mathematical expression is defined as: H=−∑i=1np(i)log⁡2p(i)H=-\\sum_{i=1}^{n}p(i)\\log_2 p(i)H=−∑i=1n​p(i)log2​p(i) In RS Metrics the formula is determined as: Diversity=−∑i=1services(count(i)recommendations)log⁡2(count(i)recommendations)Diversity=-\\sum_{i=1}^{services}\\left(\\frac{count(i)}{recommendations}\\right)\\log_2 \\left(\\frac{count(i)}{recommendations}\\right)Diversity=−∑i=1services​(recommendationscount(i)​)log2​(recommendationscount(i)​)","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#description","p":90},{"i":96,"t":"Type Float Min 0 Max +∞\\infty∞ info The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#output","p":90},{"i":98,"t":"recommendations without anonymous users all available services","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#prerequisites","p":90},{"i":100,"t":"Clean up​ Recommendations clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly Services Impact​ Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(i) Recommended Probability of the Services​ For each service calculate its recommended probability by dividing the number of service's occurrences found in the recommendations to the total number of recommendations Service-based product computation​ Calculation of the product of the recommended probability from previous step and the logarithmic value of it, for each service individually Shannon Entropy computation​ Computation of the overall value by summing all values from previous step","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/diversity_shannon_entropy","h":"#process-flow","p":90},{"i":108,"t":"The ratio of user hits divided by the total number of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#summary","p":106},{"i":110,"t":"The ratio of user hits divided by the total number of users (user hit: a user that has accessed at least one service that is also a personal recommendation). The metric is expressed by the formula: HitRate=hitsusersHit Rate=\\frac{hits}{users}HitRate=usershits​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#description","p":106},{"i":112,"t":"Type Float Min 0 Max +∞\\infty∞ info A value of 0 indicates that no user hits occurred.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#output","p":106},{"i":114,"t":"all available recommendations by registered users all available users","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#prerequisites","p":106},{"i":116,"t":"Retrieve user-service association​ For each user get the recommended services and the services the user accessed \"services\" files accordingly Calculate hits​ Check if the user has at least one accessed service in recommendations. If yes increase number of hits by one Calculate ratio​ Divide user hits by the total number of users","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/hit_rate","h":"#process-flow","p":106},{"i":122,"t":"The novelty metric expresses the ability of the system to recommend items not generally seen before by the population of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#summary","p":120},{"i":124,"t":"Calculating novelty of the recommender system based on the user actions using the following formula: Novelty=∑i∈R−log(p(i))∣R∣Novelty=\\frac{\\sum\\nolimits_{i \\in R}-log(p(i))}{|R|}Novelty=∣R∣∑i∈R​−log(p(i))​ For each service item iii belonging to the set of recommended services RRR calculate the portion p(i)p(i)p(i) of the times the service has been viewed to the total views of the services produced by the user actions data.","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#description","p":120},{"i":126,"t":"Type Float Min 0 Max +∞\\infty∞ info Novelty expresses the ability of the system to recommend items that are novel (not seen before) by the population of users. A smaller number expresses that more services are being recommended that the users have not seen before.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#output","p":120},{"i":128,"t":"all available recommendations associated with registered users a subset of the available user actions associated with registered users that expresses transitions to service pages","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#prerequisites","p":120},{"i":130,"t":"Clean up​ Recommendations and user actions clean up; entries removal where users or services are not found in \"users\" or \"services\" files accordingly User actions that target services​ Identify and keep user actions that express transition to target pages that are views of services. Additionally, user actions where the source and the target page belong to the same service's space are removed from the process. Calculate views for each service​ Group and count user actions that express views for each recommended service id Calculate view propability p(i) of each service​ Calculation of the view propability of each service which is the fraction of the service's views to the total service views Overall Novelty computation​ Computation of the overall value by summing the negative log of all recommended service views from previous step and dividing them by the total number of recommended services","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/novelty","h":"#process-flow","p":120},{"i":138,"t":"The percentage (%) of the division of the unique users found in recommendations to the total number of users.","s":"Summary","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#summary","p":136},{"i":140,"t":"The User Coverage is described by the formula unique_rec_usersusers\\frac{unique\\_rec\\_users}{users}usersunique_rec_users​","s":"Description","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#description","p":136},{"i":142,"t":"Type Float Min 0 Max 100 info User Coverage is 0 when recommendations are being suggested to none users, and 100 when recommendations are being suggested to all of the users.","s":"Output","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#output","p":136},{"i":144,"t":"all available recommendations all available users","s":"Prerequisites:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#prerequisites","p":136},{"i":146,"t":"Retrieve recommendations​ Retrieve all available recommendations found in source Gather all unique users​ Gather all unique users found in all available recommendations Retrieve users​ Retrieve all available users found in source Calculate ratio​ Calculate the percentage (%) of the division of the unique users found in recommendations to the total number of users","s":"Process Flow:","u":"/eosc-recommender-metrics/docs/metrics_descriptions/user_coverage","h":"#process-flow","p":136},{"i":152,"t":"Controller details Α public limited company (societe anonyme) under the corporate name \"National Infrastructures for Technology and Research S.A.\" and the distinctive title \"GRNET S.A.\" Controller's Contact Details dpo at grnet.gr Processor Details ARGO Support Team Processor's Contact Details argo at grnet.gr Scope of this Privacy Statement National Infrastructures for Technology and Research S.A. (hereinafter referred to as \"GRNET SA\") is bound by European Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation -- hereinafter referred to as \"the GDPR\") and Law 4624/2019 (Government Gazette 137/A/2019) on \"Data Protection Authority, measures for the implementation of Regulation(EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and for the incorporation into national law of Directive(EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 and other provisions\", as in force at any time (hereinafter referred to as \"the Law\"). This Privacy Statement details all information necessary for the processing of personal data carried out in the context of Argo Service Monitoring., as well as the policies and procedures implemented by GRNET SA for the protection of the EOSC Recommender Service - Metrics service users privacy. This Privacy Statement sets out the criteria as well as the terms and conditions under which GRNET SA collects, processes, uses, stores and transmits the personal data of the service users, how it ensures the confidentiality of such information, including any law and/or regulation implemented or enacted in accordance with Union and national laws on personal data protection and electronic privacy, as well as any law and/or regulation amending, replacing, issuing or consolidating any of the latter, including any other applicable Union and national laws on the processing of personal data and privacy, which may exist in accordance with applicable law. GRNET SA reserves the right to amend and update this Privacy Statement whenever necessary, whereas any such updates shall become effective five (5) days after they have been posted on the EOSC Recommender Service - Metrics service website. For the purposes of this Privacy Statement, the terms \"processor\", \"controller\", \"third party\", \"supervising authority\", \"personal data\", \"processing\", \"data subject\" shall have the meaning ascribed to them by applicable legislation on the protection of personal data. In addition, for the purposes of the present, the following definitions shall also apply: \"Website\" -- the website accessible via domain name web-ui-url including the entirety of the web pages thereof. \" EOSC Recommender Service - Metrics \" - web-ui-url \"User\"- the EOSC Recommender Service - Metrics online service user. The acces is restricted to specific user. The dentity is unknown or may not be verified.","s":"Privacy Policy","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"","p":151},{"i":154,"t":"i. GRNET SA-- as processor -- processes its \"users\" personal data as referred to in the following section, for the following purposes: Providing the EOSC Recommender Service - Metrics service Technical support to the \"coordinators\" of the EOSC Recommender Service - Metrics service Easy and user friendly operation of the \"EOSC Recommender Service - Metrics service\". Proof of Acceptance of the Terms of Use and the Privacy Statement Creation of statistical reports and charts to monitor the EOSC Recommender Service - Metrics service service Statistical reports and charts data do not contain any \"users\" personal data, as they result from anonymized information. ii. GRNET S.A. -- as the processor -- collects and processes personal data of \"users\" using its infrastructure for the purpose of providing the EOSC Recommender Service - Metrics service. iii. Special categories of personal data GRNET SA does not collect, process or gain access in any way to specific data categories, as set forth in the provisions of the legislation in force (in particular data relating to racial or ethnic origin, religion, health data, etc.). In the event that a \"user\" posts any such special category data on the \"website\" or on the Argo Service Monitoring, such data will be removed as soon as the management team become aware thereof.","s":"A. Purpose/s for processing the data collected:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#a-purposes-for-processing-the-data-collected","p":151},{"i":156,"t":"The processing of \"users\" personal data is necessary for the performance of the Agreement on the provision of EOSC Recommender Service - Metrics service, in accordance with the needs (technical and organisational) to provide the best possible services, to serve the \"users\".","s":"B. Legal bases for processing","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#b-legal-bases-for-processing","p":151},{"i":158,"t":"For providing EOSC Recommender Service - Metrics service and the seamless operation of such services, access to the \"users\" personal data shall be granted to the following: To the EOSC Recommender Service - Metrics service support team, consisting of personnel engaged in a contractual relationship of either a project or an independent service agreement with GRNET SA. hereinafter referred to as \"GRNET associates\". The processing of EOSC Recommender Service - Metrics service \"users\" personal data by the aforementioned, is carried out under the supervision and only at the request of GRNET SA, within the scope of the mission and the role of each associate. Such associates undertake to comply with the same privacy and personal data requirements as GRNET SA itself in accordance to the present Privacy Statement","s":"C. Access to personal data:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#c-access-to-personal-data","p":151},{"i":160,"t":"GRNET SA shall in no way transmit or in any way disclose the EOSC Recommender Service - Metrics service \"users\" personal data to any third-party entities, private businesses, natural persons or legal entities, public authorities, agencies or other organizations, other than as expressly set out herein. The Argo Service Monitoring service \"users\" personal data may be disclosed or transmitted to governmental authorities and/or law enforcement officials, only if necessary for the abovementioned purposes, in the context of enforcement of a court decision or a provision of law or if necessary to secure the legitimate interests of GRNET SA in its capacity as processor, in compliance with the terms and conditions of applicable law.","s":"D. Recipients of collected personal data:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#d-recipients-of-collected-personal-data","p":151},{"i":162,"t":"The EOSC Recommender Service - Metrics service users personal data shall be retained no longer than it is necessary for the needs of the service and the audits the service is subjected to. More specifically: Categories of personal data collected Time and place of retention of personal data IP address Data from website navigation through Cookies 18 months (log retention)","s":"E. Personal data retention periods","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#e-personal-data-retention-periods","p":151},{"i":164,"t":"The processing of personal data by GRNET SA is performed in a manner that ensures both confidentiality and security thereof. All appropriate organisational and technical measures shall be taken to safeguard data against any accidental or unlawful destruction, accidental loss, alteration, prohibited dissemination or access or any other form of unfair processing. The services provided by GRNET SA are constantly evaluated to be in line with the safety requirements of international standards. GRNET's Information Security Management System (ISMS) has been certified by the accredited certification body, EUROCERT SA In particular: Access to technical log data is restricted and can only be accessed in a secure way by the EOSC Recommender Service - Metrics service staff. When accessing the EOSC Recommender Service - Metrics servicee adequate security controls are in place to keep your personal data safe in accordance with the classification of the personal data we have collected from you. We use encryption (HTTPS) to keep data private while in transit. Data sent using HTTPS is secured via Transport Layer Security protocol (TLS), which provides: Encryption---encrypting the exchanged data to keep it secure from droppers. Data integrity---data cannot be modified or corrupted during transfer, intentionally or otherwise, without being detected. Authentication---proves that your users communicate with the intended website. The implementation of the EOSC Recommender Service - Metrics service ensures that no unauthorized user can log into the service. An authorised user means a service user, who has an active account with the Federated AAI service, having passed the authentication process mentioned above. We review our information collection, storage, and processing practices, including physical security measures, to prevent unauthorized access to our systems Although we follow best security practices to ensure your personal data remains secure, there is no absolute guarantee of security when using services online. While we strive to protect your personal data, you acknowledge that: There are security and privacy limitations on the internet which are beyond our control and can have a negative impact on the confidentiality, integrity and availability of the information. We cannot be held accountable for activity that results from your own neglect to safeguard the security of your login credentials and equipment which results in a loss of your personal data. If you feel this not enough, then please do not provide any personal data. Your personal data will be protected according to the Code of Conduct for Service Providers , a common standard for the research and higher education sector to protect your privacy.","s":"F. Privacy and Security of Information:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#f-privacy-and-security-of-information","p":151},{"i":166,"t":"For any questions or clarifications regarding the present Privacy Statement and as well as in the event of any violation related to personal data issues, \"users\" may contact the Competent Department of GRNET SA at the e-mail address mentioned hereinabove. They may also contact the Data Protection Officer (DPO) of GRNET S.A., Ms. Vera Meleti, and/or the deputy DPO, Ms. Vasiliki Konstantinopoulou at the e-mail address: dpo@grnet.gr.","s":"G. Contact:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#g-contact","p":151},{"i":168,"t":"In the event that any EOSC Recommender Service - Metrics service \"user\" request is not satisfied by the processor, the \"user\" may at any time address to/ file recourse with the Competent Supervisory Authority, namely the Data Protection Authority https://www.dpa.gr .","s":"H. Recourse/Complaint:","u":"/eosc-recommender-metrics/docs/policies/privacy","h":"#h-recoursecomplaint","p":151},{"i":170,"t":"By registering as a user you declare that you have read, understood and will abide by the following conditions of use: You shall only use the resources/services to perform work, or transmit or store data consistent with the stated goals, policies and conditions of use as defined by the body or bodies granting you access. You shall provide appropriate acknowledgement of support or citation for your use of the resources/services provided as required by the body or bodies granting you access. You shall not use the resources/services for any purpose that is unlawful and not (attempt to) breach or circumvent any administrative or security controls. You shall respect intellectual property and confidentiality agreements. You shall protect your access credentials (e.g. private keys, tokens or passwords). You shall keep all your registered information correct and up to date. You shall immediately report any known or suspected security breach or misuse of the resources/services or access credentials to the specified incident reporting locations and to the relevant credential issuing authorities. You use the resources/services at your own risk. There is no guarantee that the resources/services will be available at any time or that their integrity or confidentiality will be preserved or that they will suit any purpose. You agree that logged information, including personal data provided by you for registration purposes, may be used for administrative, operational, accounting, monitoring and security purposes. You agree that this logged information may be disclosed to other authorised participants via secured mechanisms, only for the same purposes and only as far as necessary to provide the services. You agree that the body or bodies granting you access and resource/service providers are entitled to regulate, suspend or terminate your access without prior notice and without compensation, within their domain of authority, and you shall immediately comply with their instructions. You are liable for the consequences of your violation of any of these conditions of use, which may include but are not limited to the reporting of your violation to your home institute and, if the activities are thought to be illegal, to appropriate law enforcement agencies.","s":"Terms of Use","u":"/eosc-recommender-metrics/docs/policies/terms","h":"","p":169},{"i":172,"t":"This document describes the technical and organisational measures established by National Infrastructures for Research and Technology S.A. (GRNET S.A.) to meet legal and contractual requirements when processing personal data, conducting a higher level of security and protection. The definitions in Article 32 of the GDPR apply.","s":"Technical and organisational measures (TOM)","u":"/eosc-recommender-metrics/docs/policies/tom","h":"","p":171},{"i":175,"t":"Actions suitable for providing physical and environmental security of data centers, server room facilities, and working areas are adapted, while precautions against environmental threats and power disruptions are also granted. Access is limited by job role and subject to authorised approval. Technical and organisational measures that are taken into account involve security guard personnel, reception, doorbell systems, manual locking systems, and video surveillance of entrances. Entrance to the building is only possible with keycards and keys provided to authorised employees or by visitors accompanied by employees, preventing unauthorised persons from accessing secured areas. Additionally, Information on security policy, work instructions for operational safety, and access control are provided.","s":"1.a. Physical Access Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1a-physical-access-control","p":171},{"i":177,"t":"Operations sufficient for preventing data processing systems from being used by unauthorised persons are applied. Logical access controls are designed based on authority levels and job functions. Granting access is gained on a need-to-know and least privilege basis, where it is restricted to authorised employees responsible for the job. The use of unique IDs -identified through Active Directory- and passwords for all users is adapted, including a periodic review and revoking access when employment terminates or changes in job functions occur. Technical and organisational measures that are taken into account involve; username and password protected systems, intrusion detection facilities, use of Virtual Private Networks (VPNs) for remote access, firewalls, intrusion Detection System (IDS), user permission management, information security policy, work instruction of IT user regulations, operation security and access control.","s":"1.b. Logical Access Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1b-logical-access-control","p":171},{"i":179,"t":"Actions to ensure that those authorized to use a data processing system can only access the data subject to their access authorization -based on their rule- and that personal data cannot be read, copied, modified, or removed without authorization during processing, use and after storage. Technical operations incorporate physical deletion of data carriers, logging of accesses to applications, specifically when entering, changing, and deleting data, SSH encrypted access, and certified SSL encryption. At the organisational level, a minimum number of administrators is applied, management of user rights are controlled by administrators, work instruction communication security and handling of information and values are also claimed.","s":"1.c. Authorization Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1c-authorization-control","p":171},{"i":181,"t":"It is ensured that personal data collected for different purposes can be processed separately. Multi-tenancy of relevant applications is performed, or systems are physically or logically separated. The development sheet is separated for each product, and the services have their own line of environments. All environments, documents and other data are shared for the members of that project/product, while operational, information and data protection securities and policies are applied.","s":"1.d. Separation Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1d-separation-control","p":171},{"i":183,"t":"Operations for pseudonymization or anonymization of personal data are implemented to the extent necessary. Internal instruction to pseudonymize or anonymize personal data as far as possible in the event of disclosure or even after the statutory deletion period has expired. Specific internal regulations on cryptography, while operational, information and data protection securities and policies are applied.","s":"1.e. Pseudonymization","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#1e-pseudonymization","p":171},{"i":186,"t":"Measures are taken into account to ensure that personal data cannot be read, copied, altered or removed by unauthorized persons during electronic transmission or while being transported or stored on data media. As technical and organizational actions are the use of Virtual Private Networks (VPNs) and firewalls, the provision via encrypted connections and techniques such as SSH, SFTP, HTTPS and secure cloudstores, the logging of accesses and retrievals, while operational, information and data protection securities and policies are also applied.","s":"2.a. Transfer Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#2a-transfer-control","p":171},{"i":188,"t":"Operations that ensure that it is possible to check and establish retrospectively whether and by whom personal data has been entered into, modified or removed from data processing systems are implemented to the extent necessary. Input control is achieved through logging, which can take place at various levels (e.g., operating system, network, firewall, database, application). Traceability of data entry, modification and deletion through individual user names, assignment of rights to enter, change and delete data on the basis of an authorisation concept, while information security policy and work instruction of IT user regulations are engaged.","s":"2.b. Input Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#2b-input-control","p":171},{"i":191,"t":"Actions to ensure that personal data is protected against accidental destruction or loss have been implemented to the required extent. Measures comprise fire and smoke detection systems, fire extinguishers, air-conditioning, temperature and humidity monitoring and video surveillance in server rooms, UPS system and emergency diesel generators deployment, RAID system and hard disk mirroring for data backup, information security policy and work instruction operational security.","s":"3.a. Availability Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#3a-availability-control","p":171},{"i":193,"t":"Data backups of databases and operating system images are taken to the extent required and with the aim of preventing the loss of personal data in the event of physical or technical incident. Backups are performed for network drives and servers in productive operation, where the process is being recorded (logged). The backup concept is applied according to criticality and customer specifications. When applicable storage of backup media obtained in a safe place outside the server room. Information security policy and work instruction of IT user regulations are also engaged.","s":"3.b. Recoverability Control","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#3b-recoverability-control","p":171},{"i":196,"t":"Technical and organizational measures that are taken into account are; central documentation of all data protection regulations with access for employees, security certification according to ISO 27001. Updates and reviews of the effectiveness of the TOMs are carried out periodically. Data protection checkpoints are consistently implemented, while data processing systems (IT systems) are checked regularly to the extent required and after changes to ensure that they are functioning properly. A Data Protection Officer (DPO) group is appointed and notified of physical or technical incidents, and staff is trained and obliged to confidentiality and data secrecy. Data Protection Impact Assessment (DPIA) is carried out as required, whereas processes regarding information obligations according to Art. 13 and 14 GDPR are established.","s":"4.a. Data Protection Management","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4a-data-protection-management","p":171},{"i":198,"t":"Technical and organizational actions have been established to the extent required for security breach response and data breach process. The use and the regular updating of firewall, spam filters, virus scanning, Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) for customer systems on order, are served. The process for detecting and reporting security incidents and data breaches is being documented via ticket system, with regard to reporting obligation to the supervisory authority. Formalized procedure for handling security incidents, including the involvement of DPO and ISO in security incidents and data breaches is available, while operational, information, data protection and IT user regulations, securities and policies are also applied.","s":"4.b Incident Response Management","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4b-incident-response-management","p":171},{"i":200,"t":"Measures pursuant to Art 25 GDPR comply with the principles of data protection by design and by default. No more personal data is collected than is necessary for the respective purpose. Data Protection Policy (includes principles \"privacy by design and by default\").","s":"4.c Data Protection by Design and by Default","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4c-data-protection-by-design-and-by-default","p":171},{"i":202,"t":"Actions to ensure that personal data processed on behalf of the client can only be processed in accordance with the client's instructions. Technical and organizational measures have been established to the required extent. Measures involve monitoring of remote access by external parties, in the context of remote support and work instruction supplier management and supplier evaluation. Moreover, a prior review of the security measures taken by the contractor and their documentation is applied. Selection of the contractor under due diligence aspects (especially with regard to data protection and data security) is achieved. Conclusion of the necessary data processing agreement on commissioned processing or EU standard contractual clauses and a framework agreement on contractual data processing within the group of companies, where written instructions to the contractor and obligation of the contractor's employees to maintain data secrecy. Additionally, an agreement on effective control rights over the contractor and regulations on the use of further subcontractors is maintained, ensuring also the destruction of data after termination of the contract or in the case of longer collaboration, ongoing review of the contractor and its level of protection.","s":"4.d Order Control (outsourcing, subcontractors, and order processing)","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#4d-order-control-outsourcing-subcontractors-and-order-processing","p":171},{"i":204,"t":"The National Infrastructures for Research and Technology S.A. (GRNET S.A.), based on Its Quality and Information Security Policies has set itself the goal of providing products and services to be delivered at the highest possible level of information security in compliance with the law. In this context GRNET S.A. has established, the roles of Information Security Officer (ISO), Data Protection Officer (DPO), Quality Officer (QO), and Legal Compliance Officer (LCO) as well as a Corporate Binding Rules (a set of internal guidelines and regulations) on information security and data protection, that are contractually binding for all employees, that defines secure information and data handling formed in secrecy and confidentiality. Employees are continuously informed and trained in the area of data protection, while third parties who may come into contact with personal data in the course of their work for GRNET S.A. are obligated to comply with data protection and data secrecy by means of a so-called NDA (Non-Disclosure Agreement) before they begin their work. Any subcontractors entrusted with further processing are only used after approval by the clients and after the conclusion of a Data Processing Agreement (DPA) in accordance with Art 28 GDPR, with which they are fully bound by all data protection obligations to which GRNET S.A. itself is subject. Current high technical security standards at GRNET S.A. are periodically reviewed and confirmed for adequacy and effectiveness in the course of ongoing internal audits and annually by Independent, External, Accredited Certification Bodies.","s":"5. Organization and Data Protection","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#5-organization-and-data-protection","p":171},{"i":206,"t":"The Quality Management System (QMS) as well as the Information Security Management System (ISMS) of GRNET S.A. are both certified by Independent Accredited Certification Bodies according to ISO 9001 and ISO 27001.","s":"6. Certifications","u":"/eosc-recommender-metrics/docs/policies/tom","h":"#6-certifications","p":171},{"i":209,"t":"The Recommender Metrics Framework currently monitors the Recommender System of the EOSC Marketplace/Search Service by reporting diagnostic metrics and visualizations. The Recommender System (RS) is a component of the EOSC Search Service designed to enhance the user experience by providing recommendations based on a multi-focal perspective of the users. The software calculates statistics, metrics, KPIs, and visualizations to measure the success of the RS that offer deeper insights into the performance of the system. The evaluation process involves quantitatively processing information such as resources, user actions, ratings, and recommendations to measure the impact of the AI-enhanced services and user satisfaction. The feedback obtained from the evaluation is incorporated to improve the services provided through a user-friendly API and dashboard UI.","s":"Monitoring the Recommender System of the EOSC Marketplace/Search Service","u":"/eosc-recommender-metrics/docs/use_cases","h":"#monitoring-the-recommender-system-of-the-eosc-marketplacesearch-service","p":207},{"i":211,"t":"The Recommender Metrics Framework due to its design can be used as an analysis tool of the recommendation engine of a Recommender System. Specifically, if the RMF is fed with the necessary input information such as resources, user actions, ratings, and recommendations can carry out the preprocessing stage which is retrieving data from multiple sources through a connector module that claims and transforms the data, establishing connections between service-related knowledge, eliminating dummy or irrelevant data, tagging associations in the data such as registered or anonymous users and services, and generating statistical information. Subsequently, RMF will perform the next phase's steps which are: processing the data, computing the designated evaluation metrics, and uniformly presenting the resulting information. The results such as statistics, metrics, and KPIs will be displayed through a visually appealing UI/dashboard and a REST API, with comprehensive documentation on how metrics are calculated and the expected output range.","s":"Evaluate a third-party 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