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Predicting Re-finding Activity and Difficulty

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 9022)

Abstract

In this study, we address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re-finding task. We propose to consider the task information (e.g. multiple queries and click information) rather than only queries. Our resultant prediction models are shown to be significantly more accurate (by 2%) than the current state of the art. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty.

Keywords

  • Re-finding Identification
  • Difficulty Detection
  • Behavioral Features

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  • DOI: 10.1007/978-3-319-16354-3_78
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Sadeghi, S., Blanco, R., Mika, P., Sanderson, M., Scholer, F., Vallet, D. (2015). Predicting Re-finding Activity and Difficulty. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_78

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  • DOI: https://doi.org/10.1007/978-3-319-16354-3_78

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16353-6

  • Online ISBN: 978-3-319-16354-3

  • eBook Packages: Computer ScienceComputer Science (R0)