Communications of the ACM - Special issue on parallelism
Machine Learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Incorporating confidence in a naive bayesian classifier
UM'05 Proceedings of the 10th international conference on User Modeling
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Personalized implicit learning in a music recommender system
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items. The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile. In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.