doi：10.16186/j.cnki.1673-9787.2021050117

Received:2021/05/26

Revised:2022/07/18

Published:2024/01/25

A neural network recommender algorithm with bi-directional knowledge graph attention

ZHANG Qiuling, WANG Yingxi, WANG Jianfang, NING Hui, WANG Rongsheng

School of Computer Science and Technology，Henan Polytechnic University，Jiaozuo 454000，Henan，China

Abstract: Objective Recommendation system，one of the most successful application of e-commerce and online services with the main goal of analyzing a user’s history behavior and then predicting items which are of most interest to users，has become a cornerstone of today’s information dissemination.Comparing with the traditional neural network，the neural network based on knowledge graph（KG） took the building graph as the input in the recommendation system，which could combine the node information and topology for prediction，and had demonstrated good results in terms of recommendation accuracy.However，the existing methods rarely consider the symmetric relationship in the graph structure and the problem of gradient vanishing in information aggregation. Methods A bi-directional graph attention neural network recommendation algorithm （BGANR） was proposed based on the combination of knowledge graph and neural network.Firstly the graph neural network and the symmetric attention mechanism were combined.Then，without adding additional dataset dimensions，the higher-order relationships between users-items were obtained through a bidirectional symmetric embedded translation model，which aimd at embedding representations of the features of user-item information in the Knowledge Graph，so that the relationships were considered by the attention mechanism in the decision-making weights more comprehensively.The graph-based neural network was used to correct different higher-order relationships by using multi-channel activation functions during the training process of node and neighbor information，so as to increase the amount of feedback information and avoid the over-fitting in the training process. Results The Recall and NDCG metrics in Last-FM data were improved by 2.56% and 1.96% respectively，compared with the best results of state-of-the-art model. Conclusion The extensive empirical results demonstrated that BGANR could not only explore the higher-order connectivity in bi-directions，but also realize the efficient transmission of information while capturing effective collaborative signals.

Key words:bi-directional embedding;attention mechanism;knowledge graph;graph neural network