{"title":"Click-through rate prediction model based on graph networks and feature squeeze-and-excitation mechanism","authors":"Zhongqin Bi, Susu Sun, Weina Zhang, Meijing Shan","doi":"10.1108/ijwis-07-2023-0110","DOIUrl":null,"url":null,"abstract":"\nPurpose\nPredicting a user’s click-through rate on an advertisement or item often uses deep learning methods to mine hidden information in data features, which can provide users with more accurate personalized recommendations. However, existing works usually ignore the problem that the drift of user interests may lead to the generation of new features when they compute feature interactions. Based on this, this paper aims to design a model to address this issue.\n\n\nDesign/methodology/approach\nFirst, the authors use graph neural networks to model users’ interest relationships, using the existing user features as the node features of the graph neural networks. Second, through the squeeze-and-excitation network mechanism, the user features and item features are subjected to squeeze operation and excitation operation, respectively, and the importance of the features is adaptively adjusted by learning the channel weights of the features. Finally, the feature space is divided into multiple subspaces to allocate features to different models, which can improve the performance of the model.\n\n\nFindings\nThe authors conduct experiments on two real-world data sets, and the results show that the model can effectively improve the prediction accuracy of advertisement or item click events.\n\n\nOriginality/value\nIn the study, the authors propose graph network and feature squeeze-and-excitation model for click-through rate prediction, which is used to dynamically learn the importance of features. The results indicate the effectiveness of the model.\n","PeriodicalId":44153,"journal":{"name":"International Journal of Web Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Web Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijwis-07-2023-0110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
Predicting a user’s click-through rate on an advertisement or item often uses deep learning methods to mine hidden information in data features, which can provide users with more accurate personalized recommendations. However, existing works usually ignore the problem that the drift of user interests may lead to the generation of new features when they compute feature interactions. Based on this, this paper aims to design a model to address this issue.
Design/methodology/approach
First, the authors use graph neural networks to model users’ interest relationships, using the existing user features as the node features of the graph neural networks. Second, through the squeeze-and-excitation network mechanism, the user features and item features are subjected to squeeze operation and excitation operation, respectively, and the importance of the features is adaptively adjusted by learning the channel weights of the features. Finally, the feature space is divided into multiple subspaces to allocate features to different models, which can improve the performance of the model.
Findings
The authors conduct experiments on two real-world data sets, and the results show that the model can effectively improve the prediction accuracy of advertisement or item click events.
Originality/value
In the study, the authors propose graph network and feature squeeze-and-excitation model for click-through rate prediction, which is used to dynamically learn the importance of features. The results indicate the effectiveness of the model.
期刊介绍:
The Global Information Infrastructure is a daily reality. In spite of the many applications in all domains of our societies: e-business, e-commerce, e-learning, e-science, and e-government, for instance, and in spite of the tremendous advances by engineers and scientists, the seamless development of Web information systems and services remains a major challenge. The journal examines how current shared vision for the future is one of semantically-rich information and service oriented architecture for global information systems. This vision is at the convergence of progress in technologies such as XML, Web services, RDF, OWL, of multimedia, multimodal, and multilingual information retrieval, and of distributed, mobile and ubiquitous computing. Topicality While the International Journal of Web Information Systems covers a broad range of topics, the journal welcomes papers that provide a perspective on all aspects of Web information systems: Web semantics and Web dynamics, Web mining and searching, Web databases and Web data integration, Web-based commerce and e-business, Web collaboration and distributed computing, Internet computing and networks, performance of Web applications, and Web multimedia services and Web-based education.