Recommendation Method Based on Heterogeneous Information Network and Multiple Trust Relationship

Abstract

A recommendation method based on heterogeneous information networks and multiple trust relationships is proposed. Firstly, the node sequence in the heterogeneous information network is obtained through the random walk of the meta-path, and the representation vector of each node in different paths is generated. The user similarity based on the meta-path is obtained by calculating the spatial distance between the user node vectors. Then, according to the different relationships among users, different trust-relationship calculation methods are proposed, and the user similarity based on the user’s multiple trust relationship is obtained by fusing multiple trust relationships. Finally, the candidate list of Microblog text is obtained by fusing the two user similarities to achieve a personalized recommendation of Microblog text. The experimental results show that the method proposed in this study is superior to other comparison algorithms in precision, recall, F1 value and NDCG value, which shows that this method is feasible when recommending Microblog text.