关键词: 知识图谱, 协同过滤, 推荐系统, 可解释性推荐

Abstract: Recommendation system has significance in screening the useful information and improving the efficiency of information acquisition. However, sparse data and cold start are encountered in traditional recommendation systems. Therefore, combining external rating and item connotation knowledge, this paper proposes a movie recommendation model based on recurrent knowledge graph and collaborative filtering——RKGE-CF. After the full consideration on correlation between items, users and ratings, Top-[K] recommendation is adopted which using collaborative filtering based on items and users. It adds the items external additional data and user preference data to the knowledge map, extracts the dependencies between entities and builds the interactive information between users and items. Then, the model can reveal the semantics between entities and relations, improve the understanding of users’ interests to make recommendations. Different algorithms are used to fuse several recommendation results and compare it. In the training of the model, it uses multiple groups of different negative samples for comparison, to optimize the model. Finally, a new dataset is obtained for testing by mapping real movie Movielens and IMDB. Experimental results show that this model improves the accuracy of recommendation effect, and explains the reasons behind the recommendation.

Key words: knowledge graph, collaborative filtering, recommendation system, explainable recommendation