Efficient margin-based rank learning algorithms for information retrieval

Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods available, most of them need to explicitly model the relations between every pair of relevant and irrelevant documents, and thus result in an expensive training process for large collections. The goal of this paper is to propose a general rank learning framework based on the margin-based risk minimization principle and develop a set of efficient rank learning approaches that can model the ranking relations with much less training time. Its flexibility allows a number of margin-based classifiers to be extended to their rank learning counterparts such as the ranking logistic regression developed in this paper. Experimental results show that this efficient learning algorithm can successfully learn a highly effective retrieval function for multimedia retrieval on the TRECVID'03-'05 collections.(1)