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

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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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Notes

  1. http://www.grouplens.org/

  2. http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html

  3. http://www.netflixprize.com/faq

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Cagliari, Via Ospedale 72, 09124, Cagliari, Italy

    Ludovico Boratto & Salvatore Carta

Authors
  1. Ludovico Boratto
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  2. Salvatore Carta
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Correspondence to Ludovico Boratto.

Additional information

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

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