Knowledge graph can effectively alleviate problems of data sparse and cold start in traditional collaborative filtering. Therefore, it has become an important exploration direction to integrate knowledge graph into the recommender system. However, most of the methods divide the network structure of knowledge graph into separate paths or only use the first-order neighbor information, which make it impossible to establish the high-order connectivity on the whole graph. To solve the problem, this paper proposes a KG-BGAT model which combines knowledge graph and graph attention network, and designs a bilinear collector. The bilinear collector can obtain the feature interaction message between nodes during the stage of information collection, and enrich the representation of nodes. The graph attention network propagates each node representation along the graph through the recursive embedding propagation algorithm, which can capture the high order connectivity in the graph. Top-K recommendation experiments are tested on the Movielens-1M dataset. When the length of the recommendation list is 20, the accuracy rate, recall rate and normalized discounted cumulative gain are 29.4%, 24.9% and 67.4% respectively, which exceeds the current mainstream recommendation algorithms such as CKE, RippleNet and KGCN. Experiments show that the proposed method can improve the accuracy of the recommended results.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0141