Modeling user-item profiles with neural networks for rating prediction

In recommender systems, the essential task is to predict the personalized rating of a user to a new item. To address this task, recommender systems usually employ matrix factorization model to predict ratings over a user-item rating matrix. However, this model severely suffers from the problem of data sparsity. Noting the large amount of user reviews available in the Internet, we exploit user preferences and item attributes contained in reviews to alleviate the problem. Specifically, we propose a Neural Profile-Aware Matrix Factorization model, namely NPMF, which incorporates the user and item profiles modeled with neural networks for rating prediction. We evaluate the performance of NPMF using three large-scale real-world datasets released in Yelp. The experimental results show that NPMF outperforms other mainstream rating prediction techniques and indeed alleviates the data sparsity problem.