A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions. However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour. In addition, when modeling a user's preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes. To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users' short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Specifically, we design a long-term memory generator to capture users' general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information. Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users' ongoing intent, which serves as the dominating part of recommendation. Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively. Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users' long-term behaviour.