Modeling Multidimensional User Preferences for Collaborative Filtering

A popular idea in collaborative filtering is to map users and items to latent vectors in the same Euclidean space and make recommendations based on their inner products. The idea of user/item clustering has also been exploited. However, the possibility of obtaining latent user and item feature vectors from user/item clusters has not been investigated. In this paper, we propose such a method for implicit feedback data. We cluster users along multiple latent dimensions, with each latent dimension being defined by a distinct subset of items. User clustering along a latent dimension results in two soft groups of users: those who have a tendency to consume the corresponding items and those who do not. The first group is called a taste group. As there are multiple latent dimensions, we get multiple taste groups. We map users and items to latent feature vectors based on the taste groups such that the vector for a user tells us what tastes she possesses, and the vector for an item tells us how popular it is for users with various tastes. We call the method Multidimensional User Clustering for Collaborative Filter (MUC-CF). In comparison with other methods, MUC-CF leads to more meaningful latent factors and hence its recommendations are easier to explain. MUC-CF is also scalable and in empirical evaluations, it outperforms state-of-the-art baselines.