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.
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https://hdl.handle.net/11365/22282
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