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

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 p...

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Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Yang, Deqing, Song, Zengcun, Xue, Lvxin, Xiao, Yanghua
Format: Article
Language:English
Subjects:
Artificial neural networks
Knowledge bases (artificial intelligence)
Recommender systems
Representations
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2006.10233