A Block Mixture Model for Pattern Discovery in Preference Data (Contributo in atti di convegno)

Type
Label
  • A Block Mixture Model for Pattern Discovery in Preference Data (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/ICDMW.2010.59 (literal)
Alternative label
  • Nicola Barbieri, Massimo Guarascio, Giuseppe Manco (2010)
    A Block Mixture Model for Pattern Discovery in Preference Data
    in 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - International Workshop on Topic Feature Discovery and Opinion Mining (TFDOM), Sydney, NSW; Australia, 14 December 2010 through 17 December 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Nicola Barbieri, Massimo Guarascio, Giuseppe Manco (literal)
Pagina inizio
  • 1100 (literal)
Pagina fine
  • 1107 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR (literal)
Titolo
  • A Block Mixture Model for Pattern Discovery in Preference Data (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-0-7695-4257-7 (literal)
Abstract
  • This paper presents a probabilistic co-clustering approach to pattern discovery in preference data. We extended the original formulation of the block mixture model to handle rating data, the resulting model allows the simultaneous clustering of users and items in homogeneous user communities and item categories. The parameter of the model are determined using a variational approximation and a two-phase application of the EM algorithm. The experimental evaluation showed that proposed approach can be used both for rating prediction and pattern discovery tasks, such as the analysis of common trends within the same user community and the identification of interesting relationships between products belonging to the same item category. In particular, using Movie Lens data, we show how it is possibile to infer topics for each item category, and how to model community interests and transition among topics of interest. (literal)
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