Ranking with Unlabeled Data: A first Study
Nicolas Usunier, Vinh Truong, Massih-Reza Amini, Patrick Gallinari
Laboratoire d'Informatique Paris 6
8, rue du capitaine scott
In this paper, we present a general learning framework which treats the ranking problem for various Information Retrieval tasks. We extend the training set generalization error bound proposed by Matti Kaariannan to the ranking case and show that the use of unlabeled data can be beneficial for learning a ranking function. We finally discuss open issues regarding the use of the unlabeled data during training a ranking function.