Incremental learning to rank with partially-labeled data
Kye-Hyeon Kim, Seungjin Choi · 2009
In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "un-labeled" web pages (click logs of which are not given) into learning a ranking function. In order to cope with scalability which existing semi-supervised learning methods suffer from, we develop a scalable and incremental method for semi-supervised learning to rank. In the graph regularization framework, we first determine features which well reflects data manifold and then make use of them to train a linear ranking function. We introduce a matrix-fee technique where we compute the eigenvectors of a huge similarity matrix without constructing the matrix itself. Then we present an incremental algorithm to learn a linear ranking function using features determined by projecting data onto the eigenvectors of the similarity matrix, which can be applied to a task of web-scale ranking. We evaluate our method on Live Search query log, showing that search performance is much improved when Live Search yields unsatisfactory search results.