AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder

TL;DR: Zhang et al. as discussed by the authors proposed a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths and achieved good results in terms of AUC and Precision@K.

Abstract: More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. However, most KG based recommendation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. 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. Our RNNs are encoded with full consideration of 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.