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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Rigutini, L., Papini, T., Maggini, M., Bianchini, M. (2008). A Neural Network Approach for Learning Object Ranking. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_93
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DOI: https://doi.org/10.1007/978-3-540-87559-8_93
Publisher Name: Springer, Berlin, Heidelberg
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