Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
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
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Influences of customer preference development on the effectiveness of recommendation strategies
Electronic Commerce Research and Applications
How to best characterize the personalization construct for e-services
Expert Systems with Applications: An International Journal
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
Recommendation method that considers the context of product purchases
WSEAS Transactions on Information Science and Applications
Vlogging: A survey of videoblogging technology on the web
ACM Computing Surveys (CSUR)
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
Source credibility model for neighbor selection in collaborative web content recommendation
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Sequence-based trust in collaborative filtering for document recommendation
International Journal of Human-Computer Studies
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
An efficient context-aware personalization technique in ubiquitous environments
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
Prediction of members' return visit rates using a time factor
Electronic Commerce Research and Applications
QA document recommendations for communities of question-answering websites
Knowledge-Based Systems
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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With the proliferation of e-commerce on the Web, e-commerce providers will need to offer recommender systems if they wish to remain competitive. One of the most successful recommendation methods is collaborative filtering. To provide recommendations, conventional CF methods use only a single recommender group (that is, a single information source). Consequently, they have several limitations that make them unsuitable for high-involvement, knowledge-intensive product domains such as e-learning. A new CF method, based on group behavior theory from consumer psychology, attempts to overcome these limitations. To adapt CF to Web-based e-learning content services, this method forms dual recommender groups: similar users and expert users. In experiments, a recommender system employing this method outperformed conventional CF methods in situations involving variations in the product domain and in data sparsity. This article is part of a special issue on recommender systems.