Abstract
Recommender Systems (RSs) generate personalized suggestions to users for items that may be interesting for them. Many RSs use the Collaborative Filtering (CF) technique, where the system gathers some information about the users by eliciting their ratings for items. To do so, the system may actively choose the items to present to the users to rate. This proactive approach is called Active Learning (AL), since the system actively search for relevant data before building any predictive model of the user interests. But, since not all the ratings will improve the accuracy in the same way, finding the best items to query the users for their ratings is challenging. In this work, we address this problem by reviewing some AL techniques and discussing their performance on the base of the experiments we made. © 2011 Springer-Verlag.