Skip to main content
SpringerLink
Log in

Evaluation of Pseudo Relevance Feedback Techniques for Cross Vertical Aggregated Search

  • Conference paper
  • First Online:
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9283))

Abstract

Cross vertical aggregated search is a special form of meta search, were multiple search engines from different domains and varying behaviour are combined to produce a single search result for each query. Such a setting poses a number of challenges, among them the question of how to best evaluate the quality of the aggregated search results. We devised an evaluation strategy together with an evaluation platform in order to conduct a series of experiments. In particular, we are interested whether pseudo relevance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combination of the underlying search engines themselves. While conducting the evaluations we gathered a number of qualitative and quantitative results and gained insights on how different users compare the quality of search result lists. In regard to the pseudo relevance feedback we found that using Wikipedia as knowledge base generally provides a benefit, unless for entity centric queries, which are targeting single persons or organisations. Our results will enable to help steering the development of cross vertical aggregated search engines and will also help to guide large scale evaluation strategies, for example using crowd sourcing techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM 2009, pp. 5–14. ACM, New York (2009)

    Google Scholar 

  2. Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems 20(4), 357–389 (2002). http://doi.acm.org/10.1145/582415.582416

    Article  Google Scholar 

  3. Arguello, J., Diaz, F., Callan, J., Carterette, B.: A methodology for evaluating aggregated search results. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 141–152. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Cai, D., Yu, S., Wen, J.R., Ma, W.Y.: Block-based web search. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 456–463. ACM (2004)

    Google Scholar 

  5. Diaz, F., Allan, J.: When less is more: Relevance feedback falls short and term expansion succeeds at hard 2005. Tech. rep., DTIC Document (2006)

    Google Scholar 

  6. Gehlen, V., Finamore, A., Mellia, M., Munafò, M.M.: Uncovering the big players of the web. In: Pescapè, A., Salgarelli, L., Dimitropoulos, X. (eds.) TMA 2012. LNCS, vol. 7189, pp. 15–28. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Harman, D.: Relevance feedback and other query modification techniques (1992)

    Google Scholar 

  8. He, B., Ounis, I.: Combining fields for query expansion and adaptive query expansion. Information Processing & Management 43(5), 1294–1307 (2007). http://linkinghub.elsevier.com/retrieve/pii/S0306457306001956

    Article  Google Scholar 

  9. Kazai, G., Kamps, J., Koolen, M., Milic-Frayling, N.: Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 205–214. ACM, New York (2011). http://doi.acm.org/10.1145/2009916.2009947

  10. Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 453–456. ACM, New York (2008). http://doi.acm.org/10.1145/1357054.1357127

  11. Kopliku, A., Pinel-Sauvagnat, K., Boughanem, M.: Aggregated search: A new information retrieval paradigm. ACM Computing Surveys (CSUR) 46(3), 41 (2014)

    Article  Google Scholar 

  12. Lam-Adesina, A.M., Jones, G.J.: Applying summarization techniques for term selection in relevance feedback. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–9. ACM (2001)

    Google Scholar 

  13. Lynam, T.R., Buckley, C., Clarke, C.L., Cormack, G.V.: A multi-system analysis of document and term selection for blind feedback. In: Proceedings of the 13th ACM International Conference on Information and Knowledge Management, pp. 261–269. ACM (2004)

    Google Scholar 

  14. Minnie, D., Srinivasan, S.: Meta search engines for information retrieval on multiple domains. In: Proceedings of the International Joint Journal Conference on Engineering and Technology (IJJCET 2011), pp. 115–118 (2011)

    Google Scholar 

  15. Montgomery, J., Si, L., Callan, J., Evans, D.: Effect of varying number of documents in blind feedback: analysis of the 2003 NRRC RIA workshop bfnumdocs experiment suite. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004 (2004)

    Google Scholar 

  16. Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Proceedings of the 1st International Conference on Scalable Information Systems, InfoScale 2006. ACM, New York (2006). http://doi.acm.org/10.1145/1146847.1146848

  17. Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 691–692. ACM (2006)

    Google Scholar 

  18. Rhodes, B.J.: Just-in-time information retrieval. Ph.D. thesis, Massachusetts Institute of Technology (2000)

    Google Scholar 

  19. Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, pp. 881–890. ACM (2010)

    Google Scholar 

  20. Schlötterer, J., Seifert, C., Granitzer, M.: Web-based just-in-time retrieval for cultural content. In: PATCH14: Proceedings of the 7th International ACM Workshop on Personalized Access to Cultural Heritage (2014)

    Google Scholar 

  21. Shokouhi, M., Azzopardi, L., Thomas, P.: Effective query expansion for federated search. In: Proceedings of the 32th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 427–434. ACM, New York (2009). http://doi.acm.org/10.1145/1571941.1572015

  22. Voorhees, E.M.: The philosophy of information retrieval evaluation. In: Peters, C., Braschler, M., Gonzalo, J., Kluck, M. (eds.) CLEF 2001. LNCS, vol. 2406, pp. 355–370. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Know-Center GmbH, Inffeldgasse 13, 8010, Graz, Austria

    Hermann Ziak & Roman Kern

Authors
  1. Hermann Ziak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Kern
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hermann Ziak .

Editor information

Editors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Toulouse , France

    Josanne Mothe

  2. Department of Computer Science, University of Neuchatel, Neuchâtel, Switzerland

    Jacques Savoy

  3. Faculteit der Geesteswetenschappen, Universiteit Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  4. Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Karen Pinel-Sauvagnat

  5. School of Computing, Dublin City University, Dublin, Ireland

    Gareth Jones

  6. LIA - CERI, Université d'Avignon et des Pays de Vaucluse, Avignon, France

    Eric San Juan

  7. Department of Information Engineering, University of Padua, Padua, Italy

    Linda Capellato

  8. of Information Engineering (DEI), University of Padua, Department, Padova, Italy

    Nicola Ferro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ziak, H., Kern, R. (2015). Evaluation of Pseudo Relevance Feedback Techniques for Cross Vertical Aggregated Search. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24027-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation