Personalizing Session-based Recommendation with Dual Attentive Neural Network

Abstract

Session-based recommendation which aims to recommend the next item in an anonymous session for users, becomes one of the most popular tasks in recommendation area. Traditional methods such as matrix factorization and item-to-item perform very poorly because they only take into account the last click of the session and ignore the information of the whole click sequence. On the other hand, Recurrent Neural Network (RNN) based methods have performed excellently in session-based recommendation. However, they only consider the user’s sequential behavior in the current session or only use cross-session information to track user’s interests over time, whereas the user preference is not emphasized in cross-session. Therefore, in this work, we design a novel neural network framework for personalized session-based recommendation, named Dual Attentive Neural Network (DANN). DANN considers user’s main purpose of current session and user’s personalized preference of cross-session. Specifically, in DANN we exploit a user-level attention mechanism to model user’s personalized preference and capture user’s main purpose in the current session via a session-level attention mechanism. The experimental results on two real-world datasets show that our DANN model outperforms other baseline models. Furthermore, we find that DANN achieves improvement when modeling user personalized preferences, which shows the advantage of modeling user’s preference and user’s purpose simultaneously.