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Reformulate or Quit: Predicting User Abandonment in Ideal Sessions

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Information Retrieval Technology (AIRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9994))

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Abstract

We present a comparison of different types of features for predicting session abandonment. We show that under ideal conditions for identifying topical sessions, the best features are those related to user actions and document relevance, while features related to query/document similarity actually hurt prediction abandonment.

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Author information

Authors and Affiliations

  1. University of Delaware, Newark, USA

    Mustafa Zengin & Ben Carterette

Authors
  1. Mustafa Zengin
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  2. Ben Carterette
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Corresponding author

Correspondence to Mustafa Zengin .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Shaoping Ma

  2. Renmin University of China , Beijing, China

    Ji-Rong Wen

  3. Tsinghua University , Beijing, China

    Yiqun Liu

  4. Renmin University of China , Beijing, China

    Zhicheng Dou

  5. Tsinghua University , Beijing, China

    Min Zhang

  6. Yahoo Labs , Sunnyvale, California, USA

    Yi Chang

  7. Renmin University of China , Beijing, China

    Xin Zhao

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© 2016 Springer International Publishing AG

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Zengin, M., Carterette, B. (2016). Reformulate or Quit: Predicting User Abandonment in Ideal Sessions. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-48051-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48050-3

  • Online ISBN: 978-3-319-48051-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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