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Introduction

  • Chapter
Learning to Rank for Information Retrieval

Abstract

In this chapter, we give a brief introduction to learning to rank for information retrieval. Specifically, we first introduce the ranking problem by taking document retrieval as an example. Second, conventional ranking models proposed in the literature of information retrieval are reviewed, and widely used evaluation measures for ranking are mentioned. Third, the motivation of using machine learning technology to solve the problem of ranking is given, and existing learning-to-rank algorithms are categorized and briefly depicted.

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Notes

  1. 1.

    http://www.cs.uiowa.edu/~asignori/web-size/.

  2. 2.

    http://www.iht.com/articles/2005/08/15/business/web.php.

  3. 3.

    http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html.

  4. 4.

    http://therawfeed.com/.

  5. 5.

    http://research.microsoft.com/~LETOR/.

  6. 6.

    http://glinden.blogspot.com/2005/06/msn-search-and-learning-to-rank.html, http://www.ysearchblog.com/2008/07/09/boss-the-next-step-in-our-open-search-ecosystem/.

  7. 7.

    Note that there are many different definitions of TF and IDF in the literature. Some are purely based on the frequency and the others include smoothing or normalization [70]. Here we just give some simple examples to illustrate the main idea.

  8. 8.

    The name of the actual model is BM25. In the right context, however, it is usually referred to as “OKapi BM25”, since the OKapi system was the first system to implement this model.

  9. 9.

    If there are web pages without any inlinks (which is usually referred to as dangling nodes in the graph), some additional heuristics is needed to avoid rank leak.

  10. 10.

    http://trec.nist.gov/.

  11. 11.

    This kind of judgment can also be mined from click-through logs of search engines [41, 42, 63].

  12. 12.

    Note that this is not a complete introduction of evaluation measures for information retrieval. There are several other measures proposed in the literature, some of which even consider the novelty and diversity in the search results in addition to the relevance. One may want to refer to [2, 17, 56, 91] for more information.

  13. 13.

    For a more comprehensive introduction to the machine learning literature, please refer to [54].

  14. 14.

    In this book, when we mention the output space, we mainly refer to the second type.

  15. 15.

    In the literature of machine learning, there is a topic named label ranking. It predicts the ranking of multiple class labels for an individual document, but not the ranking of documents. In this regard, it is largely different from the task of ranking for information retrieval.

  16. 16.

    We will make further discussions on the relationship between relevance feedback and learning to rank in Chap. 2.

  17. 17.

    Note that, in this book, when we refer to a document, we will not use d any longer. Instead, we will directly use its feature representation x. Furthermore, since our discussions will focus more on the learning process, we will always assume the features are pre-specified, and will not purposely discuss how to extract them.

  18. 18.

    See http://blog.searchenginewatch.com/050622-082709, http://blogs.msdn.com/msnsearch/archive/2005/06/21/431288.aspx, and http://glinden.blogspot.com/2005/06/msn-search-and-learning-to-rank.html.

  19. 19.

    Please distinguish the judgment for evaluation and the judgment for constructing the training set, although the process may be very similar.

  20. 20.

    Hereafter, when we mention the ground-truth labels in the remainder of the book, we will mainly refer to the ground-truth labels in the training set, although we assume every document will have its intrinsic label, no matter whether it is judged or not.

  21. 21.

    Similar treatment can be found in the definition of Rank Correlation in Sect. 1.2.2.

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Liu, TY. (2011). Introduction. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_1

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