Subjective Review-based Reputation (Contributo in atti di convegno)

Type
Label
  • Subjective Review-based Reputation (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2245276.2232113 (literal)
Alternative label
  • Costantino Gianpiero, Morisset Charles, Petrocchi Marinella (2012)
    Subjective Review-based Reputation
    in 27th Annual ACM Symposium on Applied Computing, SAC 2012, Trento, Italy, 26 March 2012 through 30 March 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Costantino Gianpiero, Morisset Charles, Petrocchi Marinella (literal)
Pagina inizio
  • 2029 (literal)
Pagina fine
  • 2034 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • ID_PUMA; /cnr.iit/2012-A2-036 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=2232113 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of the 27th Annual ACM Symposium on Applied Computing (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 6 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IIT, Pisa, Italy; CNR-IIT, Pisa, Italy; CNR-IIT, Pisa, Italy (literal)
Titolo
  • Subjective Review-based Reputation (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0857-1 (literal)
Abstract
  • The choice of a product or a service is often influenced by its reputation, which is usually calculated from existing reviews of this product or service. A review can be either objective, for instance when referring to concrete features of a product, or subjective, for instance when referring to the feeling of the reviewer about one aspect. Subjective reviews are potentially biased by the characteristics of the reviewers, and therefore two subjective reviews should not be treated equally. We propose in this paper a model of reputation compensating the subjective bias of di?erent categories of reviewers. We firstly calculate this bias by analyzing the ratio between reviews coming from different categories, and then we project a subjective reputation for a given category of reviewer. We demonstrate the accuracy of our bias calculation with an experimentation on public reviews for hotels, and two specific categories of users (literal)
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