TKMBR: Temporal Knowledge Graph-based Multi-Behavior Recommendation for E-commerce

Abstract Striving to enhance predictive performance by leveraging auxiliary behaviors, multi-behavior recommendation models have emerged in the realm of e-commerce. These models aim to address the diversity and effectiveness of interactive behaviors. While some methods have shown promising effects, they still exhibit certain limitations, such as overlooking dynamic nature of user interactions. In this paper, we present TKMBR, a multi-behavior recommendation framework based on a temporal knowledge graph in e-commerce. TKMBR incorporates a temporal knowledge graph to capture the temporal dynamics of user behaviors, which allows for the identification of underlying temporal patterns and the capturing of evolving user preferences over time. To augment the understanding of user preferences, heterogeneous signals are integrated and an item-side information knowledge graph is constructed based on various user-item interactions. Moreover, contrastive learning tasks are employed to alleviate the issue of data sparsity. Finally, we evaluate the performance of our approach on two representative recommendation datasets using standard metrics with HR and NDCG. Experimental results demonstrate the effectiveness of TKMBR in improving recommendation quality.