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Improving Ranking Evaluation Employing Visual Analytics

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8138)

Abstract

In order to satisfy diverse user needs and support challenging tasks, it is fundamental to provide automated tools to examine system behavior, both visually and analytically. This paper provides an analytical model for examining rankings produced by IR systems, based on the discounted cumulative gain family of metrics, and visualization for performing failure and “what-if” analyses.

Keywords

  • Information Retrieval
  • Failure Analysis
  • Ranking Model
  • Information Retrieval System
  • Ranking List

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Angelini, M., Ferro, N., Santucci, G., Silvello, G. (2013). Improving Ranking Evaluation Employing Visual Analytics. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds) Information Access Evaluation. Multilinguality, Multimodality, and Visualization. CLEF 2013. Lecture Notes in Computer Science, vol 8138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40802-1_4

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  • DOI: https://doi.org/10.1007/978-3-642-40802-1_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40801-4

  • Online ISBN: 978-3-642-40802-1

  • eBook Packages: Computer ScienceComputer Science (R0)