Sparse embeddings for recommender systems with knowledge graphs

Collaborative filtering models have undoubtedly dominated the scene of recommender systems. However, these methods do not take into account valuable item characteristics. On the other side, content-based algorithms only use this kind of information and may fail to generalize. Some collaborative filtering techniques have recently used side information about items, but they end up being huge models using thousands of features for modeling a single user-item interaction. In this paper, we present KGFlex, a sparse and expressive model based on feature embeddings. KGFlex studies which features are considered by each user when consuming an item. Then, it models each user-item interaction as a factorized entropy-driven combination of the only item features relevant to the user. An extensive experimental evaluation shows the approach's effectiveness, considering the recommendation results' accuracy, diversity, and induced bias.