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A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to ask new users to rate a few items to reveal their preferences and to use active learning to find optimally informative items. Compared to the application of active learning in classification (regression), active learning in recommender systems presents several differences: although there...
A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to use active learning techniques and ask the new user to rate a few items to reveal her preferences. The sequence of queries should not be static, i.e in each step the best query depends on the responses of the new user to the previous queries. Decision trees have been proposed to capture...
Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal...
Recommender systems help Web users to address information overload. However, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. In this paper, we consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user...
Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information...
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