Ranking-Based Implicit Regularization for One-Class Collaborative Filtering

One-class collaborative filtering (OCCF) problems are prevalent in real-world recommender systems, such as news and music recommendation, but suffer from sparsity issues and particularly lack negatively preferred items. To address the challenge, the state-of-the-art algorithms assigned uninteracted items smaller weights of being negative and performed low-rank approximation over a user-item interaction matrix. However, the prior ratings of uninteracted items are usually set to zero but may not well-defined. To avert the prior ratings of uninteracted items, in this paper, we propose a novel ranking-based implicit regularizer by hypothesizing that users preference scores for uninteracted items are close to each other. The regularizer is then used in a ranking-based OCCF framework to penalize large difference of preference scores between uninteracted items. To optimize model parameters in this framework, we develop a scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to data size. Finally, we extensively evaluate the proposed algorithms with six real-world datasets. The results show that the proposed regularizer significantly improves recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback.