Abstract
The accuracy of collaborative-filtering recommender systems largely depends on the quantity and quality of the ratings added to the system over time. Active learning (AL) aims to improve the quality of ratings by selectively finding and soliciting the most informative ratings. However previous AL techniques have been evaluated assuming a rather artificial scenario: where AL is the only source of rating acquisition. However, users do frequently rate items on their own, without being prompted by the AL algorithms (natural acquisition). In this paper we show that different AL strategies work better under different conditions, and adding naturally acquired ratings changes these conditions and may result in a decreased effectiveness for some of them. While we are unable to control the naturally occurring changes in conditions, we should adaptively select the AL strategies which are well suited for the conditions at hand. We show that choosing AL strategies adaptively outperforms any of the individual AL strategies.