Scalable Semi -supervised Preference Learning for Web Search
Kye-Hyeon Kim, Seungjin Choi · 2011
In this paper, we present a novel method for learning to rank, which is capable of semi-supervised learning by utilizing both click-through logs and the similarities between web pages simultaneously. To achieve web-scale semi-supervised learning, we develop a matrix-free algorithm that extracts latent features from a given set of web pages, where the huge similarity matrix of the web pages is not needed. Moreover, we present an incremental algorithm for our semi-supervised preference learning framework. Experiments on the Microsoft Live Search query log data show that our method effectively improves the ranks of relevant web pages of a given query, which are underestimated by Microsoft Live Search.