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

• URL: http://arxiv.org/abs/2208.13330v1
• Date: Mon, 29 Aug 2022 01:46:42 GMT
• Title: Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems
• Abstract summary: We propose a Time-aware Self-Attention with Neural Collaborative Reasoning (TiSANCR) based recommendation model. TiSANCR integrates temporal patterns and self-attention mechanism into reasoning-based recommendation. Experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms state-of-the-art recommendation methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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