The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item rating...
The goal of collaborative filtering is to make recommendations for a test user by utilizing the rating information of users who share interests similar to the test user. Because r...
Predicting items a user would like on the basis of other users' ratings for these items has become a well-established strategy adopted by many recommendation services on the ...
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replac...
Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most infor...