Search

More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. Most KG based recommendation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. However, the current approachs that focus on indedpent paths ignore the association between paths. Therefore, in this study, we propose a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths. Specifically, the paths between the user and the item are coded by a specified RNN (MRNN) to accurately learn the user's preferences. Traditional RNNs can encode multiple paths without considering the association between paths, but our RNN can encode multiple paths with considering the association between paths. We have compared AGRE with other state-of-the-art algorithms on three real-world datasets, and achieved good results in terms of AUC and Precision@K. This indicates that AGRE could solve the problem of sparse interaction between users and items, and could make full use of the knowledge graph for recommendation. • We consider the association between paths when encoding multiple paths. • We design an improved RNN. By this RNN, we can encode multiple paths. • We conduct experiments on three datasets. The experiments prove that our algorithm outperforms the baselines. • Compared with other algorithms, our algorithm can better solve the data sparse problem. [ABSTRACT FROM AUTHOR]