文献类型：期刊文献

英文题名：Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning

作者：Jiang, Liwei[1];Yan, Guanghui[1,2];Luo, Hao[1,2,3];Chang, Wenwen[1,2]

第一作者：Jiang, Liwei

通信作者：Yan, GH[1];Yan, GH[2]

机构：[1]Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]Key Lab Media Convergence Technol & Commun, Lanzhou 730030, Peoples R China;[3]Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730070, Peoples R China

第一机构：Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China

通信机构：[1]corresponding author), Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]corresponding author), Key Lab Media Convergence Technol & Commun, Lanzhou 730030, Peoples R China.

年份：2023

卷号：12

期号：20

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85175236004);WOS:【SCI-EXPANDED(收录号:WOS:001089531500001)】；

基金：Thanks to the anonymous reviewers and editors for their valuable comments and suggestions.

语种：英文

外文关键词：knowledge graph; contrastive learning; graph convolution neural network; graph attention network; recommendation system

摘要：A recommendation algorithm combined with a knowledge graph enables auxiliary information on items to be obtained by using the knowledge graph to achieve better recommendations. However, the recommendation performance of existing methods relies heavily on the quality of the knowledge graph. Knowledge graphs often contain noise and irrelevant connections between items and entities in the real world. This knowledge graph sparsity and noise significantly amplifies the noise effects and hinders the accurate representation of user preferences. In response to these problems, an improved collaborative recommendation model is proposed which integrates knowledge embedding and graph contrastive learning. Specifically, we propose a knowledge contrastive learning scheme to mitigate noise within the knowledge graph during information aggregation, thereby enhancing the embedding quality of items. Simultaneously, to tackle the issue of insufficient user-side information in the knowledge graph, graph convolutional neural networks are utilized to propagate knowledge graph information from the item side to the user side, thereby enhancing the personalization capability of the recommendation system. Additionally, to resolve the over-smoothing issue in graph convolutional networks, a residual structure is employed to establish the message propagation network between adjacent layers of the same node, which expands the information propagation path. Experimental results on the Amazon-book and Yelp2018 public datasets demonstrate that the proposed model outperforms the best baseline models by 11.4% and 11.6%, respectively, in terms of the Recall@20 evaluation metric. This highlights the method's efficacy in improving the recommendation accuracy and effectiveness when incorporating knowledge graphs into the recommendation process.