  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.
