Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Web page design: implications of memory, structure and scent for information retrieval
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Machine Learning
ACM Transactions on Internet Technology (TOIT)
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Customized Advertising via a Common Media Distributor
Marketing Science
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Marketing Science
When More Alternatives Lead to Less Choice
Marketing Science
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Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation. This paper was accepted by Sandra Slaughter, information systems.