Collaborative filtering process in a whole new light
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Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and e-commerce applications. These systems combine information retrieval and data mining techniques to provide recommendations for products, based on suggestions of users with similar preferences. Nearest-neighbor CF process is influenced by several factors, which were not examined carefully in past work. In this paper, we bring to surface these factors in order to identify existing false beliefs. Moreover, by being able to view the "big picture" from the CF process, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% percent increase in precision in comparison to widely-used CF algorithms. We perform an extensive experimental evaluation, with several real data sets, and produce results that invalidate some existing beliefs and illustrate the superiority of the proposed extensions. © 2006 IEEE.
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Elahi M; Braunhofer M; Ricci F; Tkalcic M (Springer, 2013)Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this ...
Blédaité, L; Ricci, F (ACM, 2015)The research and development of recommender systems is dominated by models of user's preferences learned from ratings for items. However, ratings have several disadvantages, which we discuss, and in order to address these ...
Elahi M; Repsys V; Ricci F (Springer, 2011)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 ratings. In general, the more ratings are elicited ...