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Journal of Intelligent Information Systems

, Volume 45, Issue 2, pp 221–245 | Cite as

The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation

Article

Abstract

A recommender system suggests items to users by predicting what might be interesting for them. The prediction task has been highlighted in the literature as the most important one computed by a recommender system. Its role becomes even more central when a system works with groups, since the predictions might be built for each user or for the whole group. This paper presents a deep evaluation of three approaches, used for the prediction of the ratings in a group recommendation scenario in which groups are detected by clustering the users. Experimental results confirm that the approach to predict the ratings strongly influences the performance of a system and show that building predictions for each user, with respect to building predictions for a group, leads to great improvements in the accuracy of the recommendations.

Keywords

Group recommendation Clustering Rating prediction 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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