  Knowledge graph (KG), integrating complex information and containing rich
semantics, is widely considered as side information to enhance the
recommendation systems. However, most of the existing KG-based methods
concentrate on encoding the structural information in the graph, without
utilizing the collaborative signals in user-item interaction data, which are
important for understanding user preferences. Therefore, the representations
learned by these models are insufficient for representing semantic information
of users and items in the recommendation environment. The combination of both
kinds of data provides a good chance to solve this problem. To tackle this
research gap, we propose a novel duet representation learning framework named
\sysname to fuse local information (user-item interaction data) and global
information (external knowledge graph) for the top-$N$ recommendation, which is
composed of two separate sub-models. One learns the local representations by
discovering the inner correlations in local information with a knowledge-aware
co-attention mechanism, and another learns the global representations by
encoding the knowledge associations in global information with a relation-aware
attention network. The two sub-models are jointly trained as part of the
semantic fusion network to compute the user preferences, which discriminates
the contribution of the two sub-models under the special context. We conduct
experiments on two real-world datasets, and the evaluations show that KADM
significantly outperforms state-of-art methods. Further ablation studies
confirm that the duet architecture performs significantly better than either
sub-model on the recommendation tasks.
