A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention

• URL: http://arxiv.org/abs/2006.10233v1
• Date: Thu, 18 Jun 2020 01:53:39 GMT
• Title: A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
• Authors: Deqing Yang and Zengcun Song and Lvxin Xue and Yanghua Xiao
• Abstract summary: We propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information. ACAM is built with a co-attention mechanism on attribute-level to achieve performance gains. Our experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models.
• Score: 16.283718738518534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement. However, most of existing attention-based models only apply item-level attention on user side, restricting the further enhancement of recommendation performance. In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains. Specifically, each user and item in ACAM are represented by a set of attribute embeddings at first. Then, user representations and item representations are augmented simultaneously through capturing the correlations between different attributes by a co-attention module. Our extensive experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models.

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