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

Abstract

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.

Effectively Using Long and Short Sessions for Multi-Session-based Recommendations

“…If all these sessions are used blindly, it will bring great noise. Therefore, we need to find the most relevant sessions in these sessions and make selective use of them [4]. The two type of sessions are actually selected according to a user's history behaviors( Similar users are also found according to the user's history) .…”

A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

“…Different from general recommendation methods, sequential recommendation is proposed to model users' dynamic interest [16][17][18]. For example, Hidasi et al [16] propose GRU4REC, which utilizes Gated Recurrent Units (GRU) to model the sequential information in a session to obtain users' dynamic interests.…”

Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration

“…To facilitate personalized recommendations, the session-aware recommender system (SARS) was developed [7]. SARS involves recommendations comprising multiple sessions [8][9][10][11]. Personalized recommendations typically rely on the user's current interaction to infer their short-term behavioral intentions.…”

“…While short-term behaviors significantly influence future interests and are thus considered short-term preferences, sessions too distant from the short-term period are classified as long-term preferences. Users have both long-term and short-term preferences integrated into their overall intention preference for recommendations [1,11]. This method's limitations include overlooking context information and a rigid definition of short-term preferences based on the last session, which can limit recommendation adaptability.…”

“…The correlation between a product and the current product represents potential information that should be considered in understanding the real intentions of consumers. Additionally, previous studies have often limited the definition of short-term preferences to the last session [11]. This approach can lead to recommendations that are too rigid and inflexible.…”