Skip to main content
View expanded cover

International Conference of the Cross-Language Evaluation Forum for European Languages

CLEF 2013: Information Access Evaluation. Multilinguality, Multimodality, and Visualization pp 29–40Cite as

Improving Ranking Evaluation Employing Visual Analytics

  • Conference paper

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-40802-1_4
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-40802-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   83.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Angelini, M., Ferro, N., Santucci, G., Silvello, G.: Visual Interactive Failure Analysis: Supporting Users in Information Retrieval Evaluation. In: Proc. of the 4th Information Interaction in Context Symposium, IIIX 2012, pp. 194–203. ACM, New York (2012)

    Google Scholar 

  2. Berkhin, P.: A Survey of Clustering Data Mining Techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  3. Geng, X., Liu, T.-Y., Qin, T., Li, H.: Feature Selection for Ranking. In: Kraaij, W., de Vries, A.P., Clarke, C.L.A., Fuhr, N., Kando, N. (eds.) Proc. 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 407–414. ACM Press, New York (2007)

    CrossRef  Google Scholar 

  4. Harman, D., Buckley, C.: Overview of the Reliable Information Access Workshop. Information Retrieval 12(6), 615–641 (2009)

    CrossRef  Google Scholar 

  5. Järvelin, K., Kekäläinen, J.: Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information System (TOIS) 20(4), 422–446 (2002)

    CrossRef  Google Scholar 

  6. Liu, T.-Y.: Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval 3(3), 225–331 (2009)

    CrossRef  Google Scholar 

  7. Liu, T.-Y.Y., Xu, J., Qin, T., Xiong, W., Li, H.: LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval. In: Joachims, T., Li, H., Liu, T.-Y., Zhai, C. (eds.) SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (2007)

    Google Scholar 

  8. Teevan, J., Dumais, S.T., Horvitz, E.: Potential for Personalization. ACM Transactions on Computer-Human Interaction (TOCHI) 17(1), 1–31 (2010)

    CrossRef  Google Scholar 

  9. van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Padua, Italy

    Nicola Ferro & Gianmaria Silvello

  2. “La Sapienza” University of Rome, Italy

    Marco Angelini & Giuseppe Santucci

Authors
  1. Marco Angelini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicola Ferro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Santucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gianmaria Silvello
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for the Evaluation of Language and Communication Technologies (CELCT), via alla Cascata 56/c, 38123, Povo, Italy

    Pamela Forner

  2. HES-SO Valais, University of Applied Sciences Western Switzerland, Technopôle 3, 3960, Sierre, Switzerland

    Henning Müller

  3. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46071, València, Spain

    Roberto Paredes

  4. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46022, València, Spain

    Paolo Rosso

  5. Bauhaus-Universität Weimar, Bauhausstraße 11, 99423, Weimar, Germany

    Benno Stein

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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)