Advertisement
Published in:
2011 | OriginalPaper | Chapter
Learning to Rank by Optimizing Expected Reciprocal Rank
Authors : Ping Zhang, Hongfei Lin, Yuan Lin, Jiajin Wu
Published in: Information Retrieval Technology
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Learning to rank is one of the most hot research areas in information retrieval, among which listwise approach is an important research direction and the methods that directly optimizing evaluation metrics in listwise approach have been used for optimizing some important ranking evaluation metrics, such as MAP, NDCG and etc. In this paper, the structural SVMs method is employed to optimize the Expected Reciprocal Rank(ERR) criterion which is named SVMERR for short. It is compared with state-of-the-art algorithms. Experimental results show that SVMERR outperforms other methods on OHSUMED dataset and TD2003 dataset, which also indicate that optimizing ERR criterion could improve the ranking performance.