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Knowledge graph is generally incorporated into recommender systems to improveoverall performance. Due to the generalization and scale of the knowledgegraph, most knowledge relationships are not helpful for a target user-itemprediction. To exploit the knowledge graph to capture target-specific knowledgerelationships in recommender systems, we need to distill the knowledge graph toreserve the useful information and refine the knowledge to capture the users'preferences. To address the issues, we propose Knowledge-aware ConditionalAttention Networks (KCAN), which is an end-to-end model to incorporateknowledge graph into a recommender system. Specifically, we use aknowledge-aware attention propagation manner to obtain the node representationfirst, which captures the global semantic similarity on the user-item networkand the knowledge graph. Then given a target, i.e., a user-item pair, weautomatically distill the knowledge graph into the target-specific subgraphbased on the knowledge-aware attention. Afterward, by applying a conditionalattention aggregation on the subgraph, we refine the knowledge graph to obtaintarget-specific node representations. Therefore, we can gain bothrepresentability and personalization to achieve overall performance.Experimental results on real-world datasets demonstrate the effectiveness ofour framework over the state-of-the-art algorithms.

Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation