{"title":"Application of graph modeling and contrast learning in recommender system","authors":"Wentao Zhang","doi":"10.54254/2755-2721/64/20241375","DOIUrl":null,"url":null,"abstract":"With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54254/2755-2721/64/20241375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.
随着个性化推荐系统在各个领域的广泛应用,如何提高推荐系统的准确性和个性化水平成为研究热点。本文提出了一种图建模与对比学习相结合的方法,通过挖掘复杂的用户项目交互和用户偏好来提高推荐系统的性能。我们首先构建了用户-项目交互图,并通过图神经网络(GNN)提取了图结构的特征。其中,使用图卷积网络(GCN)更新节点表示,并引入比较学习来优化特征表示,从而提高推荐的准确性和个性化。实验结果表明,所提出的方法在准确率、召回率和 F 1 分数上都优于传统方法。本文通过分析图建模与对比学习相结合的机理,进一步阐述了提高推荐系统性能的理论基础和实际应用,并指出了现有方法的局限性和未来的研究方向。