Session-based Recommendation via Contrastive Learning on Heterogeneous Graph

Abstract

In this work, we propose a novel session-based recommendation model which can fully leverage the intriguing relationships among items. Firstly, a heterogeneous graph with diverse edges is constructed to capture semantic information among items. Meanwhile, two challenges involved in heterogeneous graph are addressed. One is the noisy or conflict knowledge introduced by meta-path based neighbors, and the other is "disconnected graph" which is incurred by sampling from disparate types of relationships. To alleviate these problems, a global-level contrastive learning model on heterogeneous graph is designed, while we also propose an adaptive subgraph sampling algorithm and a new adaptive edge perturbation policy to cope with the isolated node problem on augmentation. Finally, a local-level fine-tuning model is followed to predict users' next behavior. Extensive experiments are performed on two real-world datasets demonstrating that the performance of our model is superior to the state-of-the-art methods.