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Evaluating Query-Independent Object Features for Relevancy Prediction

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Advances in Information Retrieval (ECIR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4425))

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Abstract

This paper presents a series of experiments investigating the effectiveness of query-independent features extracted from retrieved objects to predict relevancy. Features were grouped into a set of conceptual categories, and individually evaluated based on click-through data collected in a laboratory-setting user study. The results showed that while textual and visual features were useful for relevancy prediction in a topic-independent condition, a range of features can be effective when topic knowledge was available. We also re-visited the original study from the perspective of significant features identified by our experiments.

This work was supported by ALGRA project (TIN2004-06204-C03-02), FPU scholarship (AP2004-4678) and EPSRC (Ref: EP/C004108/1).

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Authors and Affiliations

  1. Department of Computer Science and A.I., University of Granada, Spain

    Andres R. Masegosa

  2. Department of Computing Science, University of Glasgow, UK

    Hideo Joho & Joemon M. Jose

Authors
  1. Andres R. Masegosa
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  2. Hideo Joho
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  3. Joemon M. Jose
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Giambattista Amati Claudio Carpineto Giovanni Romano

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Masegosa, A.R., Joho, H., Jose, J.M. (2007). Evaluating Query-Independent Object Features for Relevancy Prediction. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_27

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  • DOI: https://doi.org/10.1007/978-3-540-71496-5_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71494-1

  • Online ISBN: 978-3-540-71496-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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