Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph

Abstract

In view of the problem that some recommendation algorithms based on knowledge graphs only aggregate one end of the neighbors and cannot effectively determine the relationship between entities and users, this paper proposes a dual-end neighbor aggregation recommendation algorithm based on knowledge graphs. This algorithm explores the internal connections of knowledge graphs to discover the potential relationship between users and items. On the user side, this paper proposes a method of aggregating user neighbor information. In the relational space of the knowledge graph, the knowledge graph is used to spread and extract the user’s potential interest, and iteratively inject the potential interest into the user characteristics with attention bias to generate user embedding representation vector. At the item side, the user vector that aggregates the user’s neighbor information is sent to the KGCN (knowledge graph convolutional networks) model, and when the polymer product and its neighbor infor-mation are used, a new aggregation method is used to generate the item embedding representation. Finally, the obtained vector is sent to train. Through the inner product operation of the vector and normalization, the association score between the user and the item is obtained. Then the training is carried out in the training set to optimize the parameters. Comparative experiments are conducted on two public datasets. Compared with the baseline, on the Book-Crossing dataset, AUC and ACC are increased by 1.72% and 4.24%, and on the Last.FM dataset, AUC and ACC are increased by 1.07% and 1.14%. It is proven that the effectiveness of the algorithm is improved after the information of neighbors at both ends is aggregated.

Keywords

• |recommendation algorithm|knowledge graph|neighbor aggregation