We propose a fully decentralized collaborative filtering approach that is self-organizing and operates in a distributed way. The relevances between downloading files (items) are...
Jun Wang, Marcel J. T. Reinders, Reginald L. Lagen...
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this ...
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...
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are ana...
—In this paper, we describe and compare three Collaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implemen...
Manolis G. Vozalis, Angelos I. Markos, Konstantino...