Abstract

A key task in data mining and information retrieval is learning preference relations. Most of methods reported in the literature learn preference relations between objects which are represented by attribute-value pairs or feature vectors (propositional representation). The growing interest in data mining techniques which are able to directly deal with more sophisticated representations of complex objects, motivates the investigation of relational learning methods for learning preference relations. In this paper, we present a probabilistic relational data mining method which permits to model preference relations between complex objects. Preference relations are then used to rank objects. Experiments on two ranking problems for scientific literature mining prove the effectiveness of the proposed method.

Autore Pugliese

• Appice Annalisa , Loglisci Corrado , Malerba Donato , Ceci Michelangelo

Tutti gli autori

• APPICE A.;LOGLISCI C.;MALERBA D.;CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2010

ISSN

Non Disponibile

ISBN

978-1-60558-639-7

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile