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Transductive Preference Learning using Self-training

In this paper, we investigate the problem of learning user preferences in the transductive setting. The key idea of our method is to conduct a semi-supervised learning in the selftraining framework by gradually labeling unlabeled data and repeatedly re-training using the most confidently classified instance pairs. An advantage of our method is that it is able to mine and utilize the data information of the unlabeled data so as to improve the performance of the classifier. Based on this framework, we present a graph representation of the tranductive preference learning problem and formulate the preference ranking problem as a process for constructing a Hamiltonian path on a directed graph. We also develop a method for ranking top-k instances. Experimental results on several synthetic and real data sets are included to illustrate the validity and the performance of the proposed method.