Deep attention user-based collaborative filtering for recommendation

Abstract The user-based collaborative filtering (UCF) model has been widely used in industry for recommender systems. UCF predicts a user’s interest in an item based on rating information from similar user profiles. A neural network UCF model can learn effectively the high-order relations between users and items, but it cannot distinguish the importance of users in learning process. To mine the complex relationships between users and items, we incorporate a Deep+Shadow pattern to improve learning features effectively, namely as DeepUCF. Firstly, we define historical users, that is, users from the historical data of interactions with an item. The target user and historical users are calculated to capture complex process of user’s interacted item. Secondly, we integrate a shallow linear model to effectively solve single pair interaction problems. Finally, DeepUCF construct of a pair of user relations (interactive users with a history of items) for the input, and joint linear and nonlinear models to build relationships between users. More importantly, DeepUCF+a add an attention network to distinguish the historical user importance of items, which make DeepUCF more expressive. Experiments on real datasets show that DeepUCF and DeepUCF+a can effectively capture users’ complex high-order relationships, and achieve better performance.