Learning hierarchical category influence on both users and items for effective recommendation

Item category has proven to be useful additional information to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many real applications, item category is often organized in a richer knowledge structure - category hierarchy, to reflect the inherent correlations among different categories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifically, a user can be influenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the influence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms.