LS-TGNN: Long and Short-Term Temporal Graph Neural Network for Session-Based Recommendation

Zhonghong Ou, Xiao Zhang, Yifan Zhu, Shuai Lyu, Jiahao Liu, Tu Ao

Abstract: Session-Based Recommendation (SBR) based on Graph Neural Networks (GNN) has become a new paradigm for recommender systems, and plays a fundamental role in e-commerce and other relevant domains. Existing graph aggregation methods primarily form node representations by capturing basic relationships between neighboring and central nodes. Despite their encouraging results, the global relationships of items and user intentions within sessions typically change over time, which degrades the effectiveness of existing embedding schemes. To resolve this challenge, we propose a Long and Short-Term Temporal Graph Neural Network (LS-TGNN) for SBR. LS-TGNN employs a novel temporal session graph to aggregate neighborhood information, and models user interests from both long and short-term perspectives. Specifically, we design long-term and short-term encoders to model the long and short-term interests of users, respectively. In order to better model the interests of users in different time dimensions, we introduce an item-granularity method that distinguishes between long and short-term interests. Extensive experiments on three widely used datasets demonstrate that LS-TGNN outperforms existing methods with a large margin.