A Neural Network Approach for Learning Object Ranking

Authors:
Leonardo Rigutini;Tiziano Papini;Marco Maggini;Monica Bianchini
Affiliations:
Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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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.