A boosting approach for learning to rank using SVD with partially labeled data

Abstract

Learning to rank has become a hot issue in the community of information retrieval. It combines the relevance judgment information with the approaches of both in information retrieval and machine learning, so as to learn a more accurate ranking function for retrieval. Most previous approaches only rely on the labeled relevance information provided, thus suffering from the limited training data size available. In this paper, we try to use Singular Value Decomposition (SVD) to utilize the unlabeled data set to extract new feature vectors, which are then embedded in a RankBoost leaning framework. We experimentally compare the performance of our approach against that without incorporating new features generated by SVD. The experimental results show that our approach can consistently improve retrieval performance across several LETOR data sets, thus indicating effectiveness of new SVD generated features for learning ranking function. © 2009 Springer Berlin Heidelberg.