  Recommender systems are information retrieval methods that predict user
preferences to personalize services. These systems use the feedback and the
ratings provided by users to model the behavior of users and to generate
recommendations. Typically, the ratings are quite sparse, i.e., only a small
fraction of items are rated by each user. To address this issue and enhance the
performance, active learning strategies can be used to select the most
informative items to be rated. This rating elicitation procedure enriches the
interaction matrix with informative ratings and therefore assists the
recommender system to better model the preferences of the users. In this paper,
we evaluate various non-personalized and personalized rating elicitation
strategies. We also propose a hybrid strategy that adaptively combines a
non-personalized and a personalized strategy. Furthermore, we propose a new
procedure to obtain free ratings based on the side information of the items. We
evaluate these ideas on the MovieLens dataset. The experiments reveal that our
proposed hybrid strategy outperforms the strategies from the literature. We
also propose the extent to which free ratings are obtained, improving further
the performance and also the user experience.
