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.
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This work is partially funded by Regione Sardegna under project SocialGlue, through PIA - Pacchetti Integrati di Agevolazione “Industria Artigianato e Servizi” (annualità 2010).
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Boratto, L., Carta, S. The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. J Intell Inf Syst 45, 221–245 (2015). https://doi.org/10.1007/s10844-014-0346-z
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DOI: https://doi.org/10.1007/s10844-014-0346-z