Ranking with Unlabeled Data: A first Study
Nicolas Usunier, Truong Vinh, Massih Amini and Patrick Gallinari
In: NIPS'05 Workshop on Learning to Rank, 5Dec-8Dec 2005, Vancouver, Canada.
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.