{"title":"推荐系统的潜在特征建模","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":"{\"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}","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}
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.