Information Retrieval

, Volume 13, Issue 4, pp 375–397

A general approximation framework for direct optimization of information retrieval measures

Authors

    • Microsoft Research Asia
  • Tie-Yan Liu
    • Microsoft Research Asia
  • Hang Li
    • Microsoft Research Asia
Article

DOI: 10.1007/s10791-009-9124-x

Cite this article as:
Qin, T., Liu, T. & Li, H. Inf Retrieval (2010) 13: 375. doi:10.1007/s10791-009-9124-x

Abstract

Recently direct optimization of information retrieval (IR) measures has become a new trend in learning to rank. In this paper, we propose a general framework for direct optimization of IR measures, which enjoys several theoretical advantages. The general framework, which can be used to optimize most IR measures, addresses the task by approximating the IR measures and optimizing the approximated surrogate functions. Theoretical analysis shows that a high approximation accuracy can be achieved by the framework. We take average precision (AP) and normalized discounted cumulated gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that the algorithms deduced from our framework are very effective when compared to existing methods. The empirical results also agree well with the theoretical results obtained in the paper.

Keywords

Learning to rank Direct optimization of IR measures Position function approximation Truncation function approximation Accuracy analysis

Copyright information

© Springer Science+Business Media, LLC 2009