An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Category-Based Filtering in Recommender Systems for Improved Performance in Dynamic Domains
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Research Note: User Design of Customized Products
Marketing Science
Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Integrating the anchoring process with preference stability for interactive movie recommendations
HCI'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction for learning, culture, collaboration and business - Volume Part III
Evaluating books finding tools on social media: A case study of aNobii
Information Processing and Management: an International Journal
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Most previous studies on recommendation agents have been restricted to the problems of uncovering customer preferences during the process of understanding customers. However, studies on consumer psychology have indicated that customer preferences are often unstable and developed over time. Therefore, we assert that it is necessary to observe the degree to which customer preferences are developed since effectiveness of recommendations is affected by customers' preference development. This study presents a scheme to identify the status of customers' preference development and analyzes the influences of customer preference development on the effectiveness of various recommendation strategies.