HHMF: hidden hierarchical matrix factorization for recommender systems

Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF does not capture the hierarchical structural correlations, such as publisher and advertiser in advertisement recommender systems, or the taxonomy (e.g., tracks, albums, artists, genres) in music recommender systems. There are a few hierarchical MF approaches, but they require the hierarchical structures to be known beforehand. In this paper, we propose a Hidden Hierarchical Matrix Factorization (HHMF) technique, which learns the hidden hierarchical structure from the user-item rating records. HHMF does not require the prior knowledge of hierarchical structure; hence, as opposed to existing hierarchical MF methods, HHMF can be applied when this information is either explicit or implicit. According to our extensive experiments, HHMF outperforms existing methods, demonstrating that the discovery of latent hierarchical structures indeed improves the quality of recommendation.