An analysis of probabilistic methods for top-N recommendation in collaborative filtering (Contributo in atti di convegno)

Type

• Contributo in atti di convegno (Classe)
• Prodotto della ricerca (Classe)

Label

• An analysis of probabilistic methods for top-N recommendation in collaborative filtering (Contributo in atti di convegno) (literal)

Anno

• 2011-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1007/978-3-642-23780-5_21 (literal)

Alternative label

• Nicola Barbieri, Giuseppe Manco (2011)
  An analysis of probabilistic methods for top-N recommendation in collaborative filtering
  in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011, Athens; Greece, 5 September 2011 through 9 September 2011
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Nicola Barbieri, Giuseppe Manco (literal)

Pagina inizio

• 172 (literal)

Pagina fine

• 187 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

• Machine Learning and Knowledge Discovery in Databases (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• 6911 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• ICAR-CNR (literal)

Titolo

• An analysis of probabilistic methods for top-N recommendation in collaborative filtering (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-3-642-23779-9 (literal)

Abstract

• In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approches to recommendations consider the recommendation process from a \"missing value prediction\" perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation. (literal)

Prodotto di

• Analisi dei Dati per il supporto alla Sicurezza (INT.P01.010.001) (Modulo)
• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR

• NICOLA BARBIERI (Unità di personale esterno)
• GIUSEPPE MANCO (Unità di personale interno)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)
• Analisi dei Dati per il supporto alla Sicurezza (INT.P01.010.001) (Modulo)

Autore CNR di

• GIUSEPPE MANCO (Unità di personale interno)
• NICOLA BARBIERI (Unità di personale esterno)