Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems
Zhijian Luo, Zihan Huang, Jiahui Tang, Yueen Hou, Yanzeng Gao · 2024
At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in recommendation. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively.In this paper, we propose a Time-aware Self-Attention with Neural Collaborative Reasoning (TiSANCR) based recommendation model, which integrates temporal patterns and self-attention mechanism into reasoning-based recommendation. Specially, temporal patterns represented by relative time provide context and auxiliary information to characterize the user’s preference in recommendation, while self-attention is leveraged to distill informative patterns and suppress irrelevances. Extensive experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms the state-of-the-art recommendation methods.