Recommender systems based on user feedback rank items by aggregating users’ ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weig...
Florent Garcin, Boi Faltings, Radu Jurca, Nadine J...
One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This ...
Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. Current collaborati...
Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the loglikelihood of obser...
Sheng Zhang, Weihong Wang, James Ford, Fillia Make...
Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many c...
Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, ...