A general approximation framework for direct optimization of information retrieval measures

Recently direct optimization of information retrieval (IR) measures becomes a new trend in learning to rank. Several methods have been proposed and the effectiveness of them has also been empirically verified. However, theoretical justification to the algorithms was not sufficient and there were many open problems remaining. In this paper, we theoretically justify the approach of directly optimizing IR measures, and further propose a new general framework for this approach, 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 approach. We take average precision (AP) and normalized discounted cumulative gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that our approa...

• Type: Journal
• Year: 2010
• Where: IR
• Authors: Tao Qin, Tie-Yan Liu, Hang Li
• Keywords: General Framework, IR 2010, IR Measures, Natural Language Processing, Theoretical