Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Towards more conversational and collaborative recommender systems
Proceedings of the 8th international conference on Intelligent user interfaces
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Motivating participation by displaying the value of contribution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Active learning with statistical models
Journal of Artificial Intelligence Research
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Many works have been proposed in order to improve the recommendation accuracy. Algorithms aiming to improve recommendation accuracy have been developed and evaluated. These algorithms usually work with training data sets which are learned and used to make predictions on users' tastes. The training data set choice is a difficult task not only due to the technical nature of the algorithm used, but also because of the user issues associated with the acquisition of their opinions, since the training data consist of users' opinions. In this work we show the importance of understanding which predictions are impacted when a rating is acquired by developing a naïve active learning criterion based on the number of impacted predictions. To do that, a Rating Impact Analysis method for the user-based collaborative filtering is proposed and applied to the active learning issue.