This repository hosts the code and the additional materials for the paper "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation" by Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, and Markus Schedl.
You can cite this work as follows:
@inproceedings{placeholder,
author = {Marta Moscati and
Christian Wallmann and
Markus Reiter-Haas and
Dominik Kowald and
Elisabeth Lex and
Markus Schedl},
title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation},
booktitle = {Proceedings of the 17th {ACM} Conference on Recommender Systems, Singapore, September 18-22, 2023},
publisher = {{ACM}},
year = {2023},}
}
.
├── README.md
├── notebooks
└── actr_rs.ymlEdit the variables in the paths.py file as follows:
BASE_FOLDER: the main folder of the repository
- Install the environment with
conda env create -f actr_rs.yml - Activate the environment with
conda activate actr_rs
Download the dataset from Zenodo. Then run the DatasetCreation notebook. This will
- filter the [20-02-2020 -- 19-03-2020] month of the dataset
- remove users that listened to more tracks than 99% of the users
- apply 10 core filtering
- perform a 60-20-20 temporal split for each user
- create the dataset needed for training BPR, MultVAE, and GRU4Rec
This research was funded in whole, or in part, by the Austrian Science Funds (FWF): P33526 and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113.