Toward Sequential Recommendation Model for Long-Term Interest Memory and Nearest Neighbor Influence

Sequential recommendation can make predictions by fitting users' changing interests based on the users' continuous historical behavior sequences. Currently, many existing sequential recommendation methods put more emphasis upon users' recent preference (i.e., short-term interests), but simplify or even ignore the influence of users' long-term interests, resulting in important interest features of users not being effectively mined. Moreover, users' real intentions may not be fully captured by only focusing on their behavior histories, because users' interests are diverse and dynamic. To solve the above problems, we propose a novel sequential recommendation model for long-term interest memory and nearest neighbor influence. Firstly, item embeddings based on item similarity and dependency are constructed to alleviate the problem of data sparsity in users' recent interest history. Secondly, in order to effectively capture long-term interests, the long sequence is divided into multiple nonoverlapping subsequences. For these subsequences, the graph attention network with node importance factor is designed to fully extract the main interests of subsequences, and LSTM is introduced to learn the dynamic changes of interest among subsequences. Long-term interests of users are modeled through complex structure within subsequences and sequential dependencies among subsequences. Finally, the user's neighbor representation is introduced, and a gating module is designed to integrate the user's neighbor information and self-interests. The influence of users' short-term and long-term interests on prediction is dynamically controlled by considering nearby features in the gating network. The experimental results on two public datasets show that the proposed sequential recommendation model can outperform the baseline methods in hit rate (HR@K) and normalized discounted cumulative gain (NDCG@K).