In this paper, we present a connectionist approach to preference learning. In particular, a neural network is trained to realize a comparison function, expressing the preference between two objects. Such a "comparator" can be subsequently integrated into a general ranking algorithm to provide a total ordering on some collection of objects. We evaluate the accuracy of the proposed approach using the LETOR benchmark, with promising preliminary results.

Rigutini, L., Papini, T., Maggini, M., Bianchini, M. (2008). A neural network approach for learning object ranking. In Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN 2008) (pp.899-908). Springer-Verlag Berlin, Heidelberg [10.1007/978-3-540-87559-8_93].

A neural network approach for learning object ranking

RIGUTINI, LEONARDO;PAPINI, TIZIANO;MAGGINI, MARCO;BIANCHINI, MONICA
2008-01-01

Abstract

In this paper, we present a connectionist approach to preference learning. In particular, a neural network is trained to realize a comparison function, expressing the preference between two objects. Such a "comparator" can be subsequently integrated into a general ranking algorithm to provide a total ordering on some collection of objects. We evaluate the accuracy of the proposed approach using the LETOR benchmark, with promising preliminary results.
9783540875581
Rigutini, L., Papini, T., Maggini, M., Bianchini, M. (2008). A neural network approach for learning object ranking. In Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN 2008) (pp.899-908). Springer-Verlag Berlin, Heidelberg [10.1007/978-3-540-87559-8_93].
File in questo prodotto:
File Dimensione Formato  
ICANN08-rank.pdf

non disponibili

Tipologia: Post-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 226.59 kB
Formato Adobe PDF
226.59 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/22282
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo