Recommendation Model Fusing with Knowledge Graph and Collaborative Filtering

Abstract

To address the problems of existing collaborative filtering recommendation algorithms,such as low interpretability and difficulty in information extraction based on content recommendation and low recommendation efficiency,this paper proposes a hybrid recommendation model fusing with knowledge graph and collaborative filtering.The model is composed of the RCKD model and the RCKC model,the former combining knowledge graph and deep learning,and the latter combining knowledge graph and collaborative filtering.After obtaining the inference path of knowledge graph,the RCKD model uses the TransE algorithm to embed the path into vector,and captures the semantics of path inference by using LSTM and the soft attention mechanism.Then the importance of different path inferences is distinguished through pooling operation,and the prediction score is obtained through the full connection layer and the sigmoid function.According to the semantic similarity of knowledge graph representation learning,the RCKC model uses the collaborative filtering algorithm to obtain the prediction score.The two models are fused with each other according to the accuracy of the prediction score,and finally the interpretable hybrid recommendation model is obtained.The experimental results on the MovieLens data set show that the proposed model has better recommendation interpretability and higher recommendation accuracy than the RKGE model,RippleN model and the classical collaborative filtering algorithms.