Simple is Beautiful: An Online Collaborative Filtering Recommendation Solution with Higher Accuracy

摘要

Matrix factorization has high computation complexity. It is unrealistic to directly adopt such techniques to online recommendation where users, items, and ratings grow constantly. Therefore, implementing an online version of recommendation based on incremental matrix factorization is a significant task. Though some results have been achieved in this realm, there is plenty of room to improve. This paper focuses on designing and implementing algorithms to perform online collaborative filtering recommendation based on incremental matrix factorization techniques. Specifically, for the new-user and new-item problems, Moore-Penrose pseudoinverse is used to perform incremental matrix factorization; and for the new-rating problem, iterative stochastic gradient descentlearning procedure is directly applied to get the updates. The solutions seem simple but efficient: extensive experiments show that they outperform the benchmark and the state-of-the-art in terms of incremental properties.