Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106911

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106911

Title:	Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices
Authors:	張嘉惠;Wu, Meng-Lun;Chang, Chia-Hui;Liu, Rui-Zhe
Contributors:	資訊電機學院資訊工程學系
Keywords:	Applied sciences;Augmented data;Co-clustering;Collaborative filtering;Computer science;control theory;systems;Computer systems and distributed systems. User interface;Data processing. List processing. Character string processing;Exact sciences and technology;Expert systems;Filtering;Filtration;Memory organisation. Data processing;Mutual information;Ratings;Recommender system;Roots;Software
Date:	2014-05-01
Issue Date:	2026-04-23 13:48:29 (UTC+8)
Publisher:	Elsevier Ltd.;Amsterdam: Elsevier Ltd
Abstract:	摘要： •Co-clustering algorithm with augmented matrix (CCAM).•A unified framework for content-based filtering and collaborative filtering (CF).•Comparison of model-based CF and memory-based CF. Recommender systems have become an important research area because of a high interest from academia and industries. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations. However, CF often confronts the sparsity problem, caused by fewer ratings against the unknowns that need to be predicted. In this paper, we consider a hybrid approach that combines content-based approach with collaborative filtering under a unified model called co-clustering with augmented matrices (CCAM). CCAM is based on information-theoretic co-clustering but further considers augmented data matrices like user profile and item description. By presenting results with a reduced error of prediction, we show that content-based information can help reduce the sparsity problem through minimizing the mutual information loss of the three data matrices based on CCAM. 出版者： Amsterdam: Elsevier Ltd 出版日期： 2014-05-01 出處： Expert systems with applications, 2014-05, Vol.41 (6), p.2754-2761 版權： 2013 Elsevier Ltd 版權： 2015 INIST-CNRS 識別號： ISSN: 0957-4174 識別號： EISSN: 1873-6793 識別號： DOI: 10.1016/j.eswa.2013.10.008
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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