A hybrid intelligent service recommendation by latent semantics and explicit ratings

International Journal of Intelligent SystemsVolume 36, Issue 12 p. 7867-7894 RESEARCH ARTICLE A hybrid intelligent service recommendation by latent semantics and explicit ratings Li Duan, Li Duan orcid.org/0000-0002-1825-7416 School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, ChinaSearch for more papers by this authorTieliang Gao, Corresponding Author Tieliang Gao [email protected] orcid.org/0000-0001-7075-2206 Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, Xinxiang, China Correspondence Tieliang Gao, Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, 453003 Xinxiang, China.Wei Wang, School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100444 Beijing, China. Email: [email protected] and [email protected]Search for more papers by this authorWei Ni, Wei Ni orcid.org/0000-0002-4933-594X Data61, CSIRO, Sydney, New South Wales, AustraliaSearch for more papers by this authorWei Wang, Corresponding Author Wei Wang [email protected] orcid.org/0000-0002-5974-1589 School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia Correspondence Tieliang Gao, Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, 453003 Xinxiang, China.Wei Wang, School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100444 Beijing, China. Email: [email protected] and [email protected]Search for more papers by this author Li Duan, Li Duan orcid.org/0000-0002-1825-7416 School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, ChinaSearch for more papers by this authorTieliang Gao, Corresponding Author Tieliang Gao [email protected] orcid.org/0000-0001-7075-2206 Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, Xinxiang, China Correspondence Tieliang Gao, Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, 453003 Xinxiang, China.Wei Wang, School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100444 Beijing, China. Email: [email protected] and [email protected]Search for more papers by this authorWei Ni, Wei Ni orcid.org/0000-0002-4933-594X Data61, CSIRO, Sydney, New South Wales, AustraliaSearch for more papers by this authorWei Wang, Corresponding Author Wei Wang [email protected] orcid.org/0000-0002-5974-1589 School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia Correspondence Tieliang Gao, Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, 453003 Xinxiang, China.Wei Wang, School of Computer and Information Technology, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, 100444 Beijing, China. Email: [email protected] and [email protected]Search for more papers by this author First published: 15 August 2021 https://doi.org/10.1002/int.22612Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting user-service ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set. Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods. Citing Literature Volume36, Issue12December 2021Pages 7867-7894 RelatedInformation