Deep Attentive Interest Collaborative Filtering for Recommender Systems

Abstract

Collaborative filtering (CF) is a pivotal building block in commercial recommender systems due to its strength and utility in user interest modeling. Recently, many researchers have turned to deep learning as a way to capture richer collaborative signals from user-item feature interactions. However, most deep-based methods only consider nonlinear, high-order interactions while ignoring the explicit collaborative signals in low-order interactions. They also typically ignore the quality of the user and item profiles. These are cornerstones in item recommendation that, we argue, must be considered for high-quality recommendations. Hence, we propose Deep Attentive Interest Collaborative Filtering (DAICF) to overcome these limitations. DAICF profiles users based on their interactive items, i.e., user neighborhood information. Similarly, item profiles are based on users who had interacted with it, i.e., item neighborhood information. Given a user's profile varies over different items, DAICF accurately models his attentive interests based on the specific target item. Low-order collaborative signals are captured by a shallow component, and high-order collaborative signals are captured by a deep component. These two complementary collaborative signals are then fused to provide rich recommendations that cut through today's information overload. By designing a personalized feature extraction method based on bilateral neighborhood information to solve the data sparsity problem in recommender systems, DAICF can dynamically distinguish the importance of a user's historical interaction items for predicting user preferences for a specific target item. A set of experiments against four real-world datasets validate that DAICF outperforms the most recent state-of-the-art recommendation algorithms and justifies the effectiveness and interpretability of our method.