| Summary: | Knowledge graph (KG) provides a data structure to generate hybrid recommendations based on content and collaborative filtering. However, the existing recommendation methods based on knowledge graph take much less account of the user attribute information than the item attribute. To solve this problem, double end knowledge graph convolutional networks (DEKGCN) for recommender systems is proposed. In this algorithm, certain amount of sample of each entity’s neighborhood in the knowledge graph is taken as its high-order acceptance domain, and the related attributes of users in the dataset are taken as its first-order receptive field. Then, when calculating the representation of a given entity and a user, the neighborhood information is combined respectively, and finally the probability of user’s preference for items is obtained. It is an end-to-end framework that integrates multiple information of both user and item sides to learn the vector representation of users and items, which effectively solves the problem of data sparsity and cold start. Experimental results on real datasets show that DEKGCN has better recommendation quality than other baselines.
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