Impact of Content Novelty on the Accuracy of a Group Recommender System

  • Ludovico Boratto
  • Salvatore Carta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8646)


A group recommender system is designed for contexts in which more than a person is involved in the recommendation process. There are types of content (like movies) for which it would be advisable to recommend an item only if it has not yet been consumed by most of the group. In fact, it would be trivial and not significant to recommended an item if a great part of the group has already expressed a preference for it. This paper studies the impact of content novelty on the accuracy of a group recommender system, by introducing a constraint on the percentage of a group for which the recommended content has to be novel. A comparative analysis in terms of different values of the percentage of the group and for groups of different sizes, was validated through statistical tests, in order to evaluate when the difference in the accuracy values is significant. Experimental results, deeply analyzed and discussed, show that the recommendation of novel content significantly affects the performances only for small groups and only when content has to be novel for the majority of it.


Group Recommendation Content Novelty Clustering Accuracy 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ludovico Boratto
    • 1
  • Salvatore Carta
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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