河南理工大学学报（自然科学版）2024，Vol.43Issue(1)：149-156,8.DOI:10.16186/j.cnki.1673-9787.2021050117
基于双向注意力的图神经推荐算法研究
A neural network recommender algorithm with bi-directional knowledge graph attention
摘要
Abstract
Objective Recommendation system,one of the most successful application of e-commerce and on-line 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 predic-tion,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 data-set 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 mecha-nism in the decision-making weights more comprehensively.The graph-based neural network was used to cor-rect different higher-order relationships by using multi-channel activation functions during the training pro-cess 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 collabora-tive signals.关键词
双向嵌入/注意力机制/知识图谱/图神经网络Key words
bi-directional embedding/attention mechanism/knowledge graph/graph neural network分类
信息技术与安全科学引用本文复制引用
张秋玲,王滢溪,王建芳,宁辉,王荣胜..基于双向注意力的图神经推荐算法研究[J].河南理工大学学报（自然科学版）,2024,43(1):149-156,8.基金项目
国家自然科学基金资助项目(61972134) （61972134）
