Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Incorporating user control into recommender systems based on naive bayesian classification
Proceedings of the 2007 ACM conference on Recommender systems
A User Modeling Using Implicit Feedback for Effective Recommender System
ICHIT '08 Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Theoretical foundations of active learning
Theoretical foundations of active learning
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Recommender systems typically require feedback from the user to learn the user's taste This feedback can come in two forms: explicit and implicit Explicit feedback consists of ratings provided by the user for a number of items, while implicit feedback comes from observing user actions on items These actions have to be interpreted by the recommender system and translated into a rating In this paper we propose a method to learn how to translate user actions on items to ratings on these items by correlating user actions with explicit feedback We do this by associating user actions to rated items and subsequently applying naive Bayesian classification to rate new items with which the user has interacted We apply and evaluate our method on data from a web-based music service and we show its potential as an addition to explicit rating.