A Unified Latent Factor Model for Effective Category-Aware Recommendation

Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the non-negligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-to-one) or multiple categories (one-to-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.