Yuan Lin
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.
- Z. Cao, T. Qin, T. Y. Liu, M. F. Tsai, and H. Li. Learning to rank: From pairwise approach to listwise approach. In Proceedings of the ICML, pages 129--136, 2007. Google Scholar
Digital Library
- Y. Lin, H. Lin, Z. Ye, S. Jin, and X. L. Sun. Learning to rank with groups. In Proceedings of CIKM, pages 1589--1592, 2010. Google ScholarDigital Library
- T. Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009. Google ScholarDigital Library
- T. Y. Liu, J. Xu, T. Qin, W. Xiong, and H. Li. Letor: Benchmark dataset for research on learning to rank for information retrieval. In Proceedings of the Learning to Rank workshop in SIGIR, pages 3--10, 2007.Google Scholar
- F. Xia, T. Y. Liu, and H. Li. Statistical consistency of top-k ranking. In Proceedings of NIPS, pages 2098--2106, 2009.Google Scholar
Index Terms
- Learning to rank with cross entropy
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