Learning to rank with cross entropy

Authors:
Yuan Lin;Hongfei Lin;Jiajin Wu;Kan Xu
Affiliations:
Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

Citing 3
Cited 0

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Foundations and Trends in Information Retrieval
Learning to rank with groups

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management

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Abstract

Learning to rank algorithms are usually grouped into three types: the point wise approach, the pairwise approach, and the listwise approach, according to the input spaces. Much of the prior work is based on the three approaches to learn the ranking model to predict the relevance of a document to a query. In this paper, we focus on the problem of constructing new input space based on groups of documents with the same relevance judgment. A novel approach is proposed based on cross entropy to improve the existing ranking method. The experimental results show that our approach leads to significant improvements in retrieval effectiveness.