Modeling item selection and relevance for accurate recommendations: a bayesian approach (Contributo in atti di convegno)

Type
Label
  • Modeling item selection and relevance for accurate recommendations: a bayesian approach (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2043932.2043941 (literal)
Alternative label
  • Riccardo Ortale, Giuseppe Manco, Gianni Costa, Nicola Barbieri (2011)
    Modeling item selection and relevance for accurate recommendations: a bayesian approach
    in 5th ACM Conference on Recommender Systems, RecSys 2011, Chicago, 23 October 2011 through 27 October 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Riccardo Ortale, Giuseppe Manco, Gianni Costa, Nicola Barbieri (literal)
Pagina inizio
  • 21 (literal)
Pagina fine
  • 28 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR, ICAR-CNR, ICAR-CNR, _ (literal)
Titolo
  • Modeling item selection and relevance for accurate recommendations: a bayesian approach (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0683-6 (literal)
Abstract
  • We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items and tend to adopt the same rating pattern. Each user is modeled as a random mixture of topics, where each topic is characterized by a distribution modeling the popularity of items within the respective user-community and by a distribution over preference values for those items. The proposed model can be associated with a novel item-relevance ranking criterion, which is based both on item popularity and user's preferences. We show that the proposed model, equipped with the new ranking criterion, outperforms state-of-art approaches in terms of accuracy of the recommendation list provided to users on standard benchmark datasets (literal)
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