Selecting optimal training data for learning to rank

This paper is concerned with the quality of training data in learning to rank for information retrieval. While many data selection techniques have been proposed to improve the quality of training data for classification, the study on the same issue for ranking appears to be insufficient. As pointed out in this paper, it is inappropriate to extend technologies for classification to ranking, and the development of novel technologies is sorely needed. In this paper, we study the development of such technologies. To begin with, we propose the concept of “pairwise preference consistency” (PPC) to describe the quality of a training data collection from the ranking point of view. PPC takes into consideration the ordinal relationship between documents as well as the hierarchical structure on queries and documents, which are both unique properties of ranking. Then we select a subset of the original training documents, by maximizing the PPC of the selected subset. We further propose an efficient solution to the maximization problem. Empirical results on the LETOR benchmark datasets and a web search engine dataset show that with the subset of training data selected by our approach, the performance of the learned ranking model can be significantly improved.

Research highlights
► We have pointed out the difference between training data selection for ranking and that for classification.
► We have proposed using Pairwise Preference Consistency to measure the quality of a training data collection for ranking.
► We have proposed selecting high-quality training data for ranking by maximizing the PPC of the selected subset, and proposed efficient solutions.
► We have empirically verified the efficiency of the proposed approach.