Multiple Attribute Aware Personalized Ranking

Personalized ranking is a typical task of recommender systems. It can provide a set of items for specific user and help recommender systems more correctly direct each item to its user. Recently, as the dramatically increasing social media, an entity, i.e., user and item, usually associates with multiple kinds of characterized information, e.g., explicit ratings, implicit feedbacks, and multi-type attributes (such as age, sex, occupation, or posts of user). Intuitively, comprehensively considering these information, we can obtain better personalized ranking results. However, most conventional methods only take collaborative information (explicit ratings or implicit feedbacks) or single type attributes into account. In this work, we investigate how to combine multiple attribute and collaborative information to learn the latent factors for entities and the attribute-aware mappings. As a result, we propose a novel Multipleattribute-aware Bayesian Personalized Ranking model, Maa-BPR, for personalized ranking, which can learn reliable latent factors for entities as well as effective mappings for multiple attribute. The experimental results show that, compared with the state-of-the-art methods, Maa-BPR not only provides better ranking performance, but also is robust to new entities and the incomplete attributes.