{"title":"Latent Feature Modelling for Recommender Systems","authors":"Abdullah Alhejaili, S. Fatima","doi":"10.1109/IRI49571.2020.00057","DOIUrl":null,"url":null,"abstract":"Matrix factorization is one of the most successful model-based collaborative filtering approaches in recommender systems. Nevertheless, useful latent user features can lead to a more accurate recommendation. However, user privacy and cross-domains access restrictions challenge collection and analysis of such information. In this study, we propose a feature extraction method (WAFE) which leverages user-item interaction history to extract useful latent user features. We also propose a rating prediction approach that incorporates the local mean of users’ and items’ ratings. We evaluate our proposed model using two real-world benchmark datasets and compare its performance against the state-of-the-art matrix factorization collaborative filtering methods. Evaluation results show that proposed method outperforms the existing methods.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"10 1","pages":"349-356"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Matrix factorization is one of the most successful model-based collaborative filtering approaches in recommender systems. Nevertheless, useful latent user features can lead to a more accurate recommendation. However, user privacy and cross-domains access restrictions challenge collection and analysis of such information. In this study, we propose a feature extraction method (WAFE) which leverages user-item interaction history to extract useful latent user features. We also propose a rating prediction approach that incorporates the local mean of users’ and items’ ratings. We evaluate our proposed model using two real-world benchmark datasets and compare its performance against the state-of-the-art matrix factorization collaborative filtering methods. Evaluation results show that proposed method outperforms the existing methods.