Collaborative Multi-objective Ranking

This paper proposes to jointly resolve row-wise and column-wise ranking problems when an explicit rating matrix is given. The row-wise ranking problem, also known as personalized ranking, aims to build user-specific models such that the correct order of items (in terms of user preference) is most accurately predicted and then items on the top of ranked list will be recommended to a specific user, while column-wise ranking aims to build item-specific models focusing on targeting users who are most interested in the specific item (for example, for distributing coupons to customers). In recommender systems, ranking-based collaborative filtering (known as collaborative ranking (CR)) algorithms are designed to solve the aforementioned ranking problems. The key part of CR algorithms is to learn effective user and item latent factors which are combined to decide user preference scores over items. In this paper, we demonstrate that by individually solving row-wise or column-wise ranking problems using typical CR algorithms is only able to learn one set of effective (user or item) latent factors. Therefore, we propose to jointly solve row-wise and column-wise ranking problems through a parameter sharing framework which optimizes three objectives together: to accurately predict rating scores, to satisfy the user-specific order constraints on all the rated items, and to satisfy the item-specific order constraints. Our extensive experimental results on popular datasets confirm significant performance gains of our proposed method over state-of-the-art CR approaches in both of row-wise and column-wise ranking tasks.