Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Thirty years of conjoint analysis: reflections and prospects
Interfaces - Special issue: marketing engineering
E-Commerce Trust Metrics and Models
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
A survey of trust and reputation systems for online service provision
Decision Support Systems
Design of a shopbot and recommender system for bundle purchases
Decision Support Systems
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
Sequence-based trust in collaborative filtering for document recommendation
International Journal of Human-Computer Studies
PointBurst: towards a trust-relationship framework for improved social recommendations
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
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Since collaborative filtering (CF) based recommendation methods rely on neighbors as information sources, their performance depends on the quality of neighbor selection process. However, conventional CF has a few fundamental limitations that make them unsuitable for Web content services: recommender reliability problem and no consideration of customers' heterogeneous susceptibility on information sources. To overcome these problems, we propose a new CF method based on the source credibility model in consumer psychology. The proposed method extracts each target customer's part-worth on source credibility attributes using conjoint analysis. The results of the experiment using the real Web usage data verified that the proposed method outperforms the conventional methods in the personalized web content recommendation.