A query-level active sampling approach for learning to rank

Abstract

Learning to rank is becoming more and more popular in machine learning and information retrieval field. However, like many other supervised approaches, one of the main problems with learning to rank is lack of labeled data. Recently, there have been attempts to address the challenges in active sampling for learning to rank. But none of these methods take into consideration the differences between queries*. In this paper, we propose a novel active ranking framework on query-level which aims to employ different ranking models for different queries. Then, we used Rank SVM as a base ranker, realized a query-level active ranking algorithm and applied it to document retrieval. Experimental results on real-world data set show that our approach can reduce the labeling cost greatly without decreasing the ranking accuracy.