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Learning to Rank for Information Retrieval pp 241–248Cite as

Future Work

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Abstract

In this chapter, we discuss the possible future work on learning to rank. In particular, we show some potential research topics along the following directions: sample selection bias, direct learning from logs, feature engineering, advanced ranking models, large-scale learning to rank, online complexity, robust learning to rank, and online learning to rank. At the end of this chapter, we will make brief discussions on the new scenarios beyond ranking, which seems to be the future trend of search. Algorithmic and theoretical discussions on the new scenario may lead to another promising research direction.

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Authors and Affiliations

  1. Microsoft Research Asia, Bldg #2, No. 5, Dan Ling Street, Haidian District, Beijing, 100080, People’s Republic of China

    Tie-Yan Liu

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  1. Tie-Yan Liu
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Correspondence to Tie-Yan Liu .

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© 2011 Springer-Verlag Berlin Heidelberg

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Liu, TY. (2011). Future Work. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_20

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  • DOI: https://doi.org/10.1007/978-3-642-14267-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14266-6

  • Online ISBN: 978-3-642-14267-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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