Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems

TL;DR

This paper introduces TiSANCR, a recommendation model that incorporates temporal patterns and self-attention into logical reasoning, significantly improving recommendation accuracy by capturing time-aware user preferences.

Contribution

The paper proposes a novel time-aware self-attention mechanism integrated with neural collaborative reasoning for enhanced recommendation performance.

Findings

TiSANCR outperforms state-of-the-art methods on benchmark datasets.

Temporal patterns improve the understanding of user preferences.

Self-attention effectively distills relevant information from temporal data.

Abstract

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 and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively. To address these issues, 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. Therefore, the fusion of self-attentive temporal information provides deeper representation of user's preference. Extensive experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms the state-of-the-art recommendation methods.