Cross Attentive Collaborative Filtering for Recommender Systems

The information on the Internet is increasingly complicated, users can not use the search engine to accurately find effective information. But the emergence of the recommendation system can effectively alleviate this problem. Algorithms such as collaborative filtering have a large drop in recommended quality when user ratings are sparse.  We propose a novel cross attentive collaborative filtering model. Our model can learn more about the potential relationships between users, items and ratings. To validate the validity of the algorithm model, experiments were performed on public datasets. The experimental results show that the mae and rmse index have decreased.