Enabling Kernel-Based Based Attribute Attribute-Aware Aware Matrix Factorization for Rating Prediction
Abstract: In recommender systems, one key task is to predict the personalized rating of a user to a new item and then return the new items having the top predicted ratings to the user. Recommender systems usually apply collaborative filtering techniques (e.g., matrixx factorization) over a sparse user user-item item rating matrix to make rating prediction. However, the collaborative filtering techniques are severely affected by the data sparsity of the underlying user user-item item rating matrix and often confront the cold cold-start problemss for new items and users. Since the attributes of items and social links between users become increasingly accessible in the Internet, this paper exploits the rich attributes of items and social links of users to alleviate the rating sparsity effect and ttackle the cold-start start problems. Specifically, we first propose a Kernel Kernel-based Attribute-aware aware Matrix Factorization model called KAMF to integrate the attribute information of items into matrix factorization. KAMF can discover the nonlinear interactions among amon attributes, users, and items, which mitigate the rating sparsity effect and deal with the coldcold start problem for new items by nature. Further, we extend KAMF to address the cold-start start problem for new users by utilizing the social links between users. Finally, ally, we conduct a comprehensive performance evaluation for KAMF using two large-scale real-world world data sets recently released in Yelp and MovieLens.