Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.mllib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.mllib uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.mllib has the following parameters:
- numBlocks is the number of blocks used to parallelize computation (set to -1 to auto-configure).
- rank is the number of latent factors in the model.
- iterations is the number of iterations to run.
- lambda specifies the regularization parameter in ALS.
- implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data.
- alpha is a parameter applicable to the implicit feedback variant of ALS that governs the baseline confidence in preference observations.
- Explicit vs. implicit feedback
- The standard approach to matrix factorization based collaborative filtering treats the entries in the user-item matrix as explicit preferences given by the user to the item.
It is common in many real-world use cases to only have access to implicit feedback (e.g. views, clicks, purchases, likes, shares etc.). The approach used in spark.mllib to deal with such data is taken from Collaborative Filtering for Implicit Feedback Datasets. Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as a combination of binary preferences and confidence values. The ratings are then related to the level of confidence in observed user preferences, rather than explicit ratings given to items. The model then tries to find latent factors that can be used to predict the expected preference of a user for an item.
#1. Data set We will use two files from this MovieLens dataset: “ratings.dat” and “movies.dat”. All ratings are contained in the file “ratings.dat” and are in the following format:
UserID::MovieID::Rating::Timestamp Movie information is in the file “movies.dat” and is in the following format:
MovieID::Title::Genres
#2. Collaborative filtering Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix, in our case, the user-movie rating matrix. MLlib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. In particular, we implement the alternating least squares (ALS) algorithm to learn these latent factors.
Matrix Factorization
