Rating-based collaborative filtering combined with additional regularization

Shu Wu, Shengrui Wang

Abstract: The collaborative filtering (CF) approach to recommender system has received much attention recently. However, previous work mainly focuses on improving the formula of rating prediction, e.g. by adding user and item biases, implicit feedback and time-aware factors, etc, to reach a better prediction by minimizing an objective function. However, little effort has been made on improving CF by incorporating additional regularization to the objective function. Regularization can further bound the searching range of predicted ratings. In this paper, we improve the conventional rating-based objective function by using ranking constraints as the supplementary regularization to restrict the searching of predicted ratings in smaller and more likely ranges, and develop a novel method, called RankSVD++, based on the SVD++ model. Experimental results show that RankSVD++ achieves better performance than existing main-streaming methods due to the addition of informative ranking-based regularization. The idea proposed here can also be easily incorporated to the other CF models.