TKMBR: Temporal Knowledge Graph-based Multi-Behavior Recommendation
Xiaoman Zhang, Linlin Zhang, Xuehua Bi, Guanglei Yu, Ruyi Cao, Yang Chen · 2024
Striving to enhance predictive performance by leveraging auxiliary behaviors, multi-behavior recommendation models have emerged in in many different fields. 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 temporal knowledge graph-based framework for multi-behavior recommendation. 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. Evaluation on three datasets using HR and NDCG shows TKMBR’s effectiveness in improving recommendation quality.