Skip to main content

Differential Evolution-Based Fusion for Results Diversification of Web Search

  • Conference paper
  • First Online:
Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

Included in the following conference series:

Abstract

Results diversification has been a key research issue on web search in the last couple of years. Some recent research work suggests that data fusion, especially linear combination of multiple results, is a good option of dealing with this problem. However, there are many different ways of setting weights. In this paper, we propose a differential evolution-based method to find optimal weights in the weight space for the linear combination method. Experimental results show that the proposed method is effective compared with the state-of-the-art techniques.

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

Access this chapter

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 EPUB and 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

Similar content being viewed by others

Notes

  1. 1.

    TREC (Text Retrieval Conference) is an annual information retrieval evaluation event held by the National Institute of Standards and Technology, USA. Its web site is located at http://trec.nist.gov/.

  2. 2.

    Runs can be downloaded from TREC’s web site http://trec.nist.gov.

  3. 3.

    http://1boston.lti.cs.cmu.edu/Data/clueweb09

  4. 4.

    Its web site is located at http://research.nii.ac.jp/ntcir/ntcir-12/index.html.

References

  1. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of WSDM 2009, Barcelona, Spain, pp. 5–14 (2009)

    Google Scholar 

  2. Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of ACM SIGIR 2001, New Orleans, Louisiana, USA, pp. 276–284 (2001)

    Google Scholar 

  3. Bartell, B.T., Cottrell, G.W., Belew, R.K.: Automatic combination of multiple ranked retrieval systems. In: Proceedings of ACM SIGIR 1994, Dublin, Ireland, pp. 173–184 (1994)

    Google Scholar 

  4. Jaime, G., Goldstein, C.J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of ACM SIGIR 1998, Melbourne, Australia, pp. 335–336 (1998)

    Google Scholar 

  5. Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceedings of ACM CIKM 2009, Hong Kong, China, pp. 621–630 (2009)

    Google Scholar 

  6. Charles, L., Clarke, A., Craswell, N., Soboroff, I.: Overview of the TREC web track. In: Proceedings of the Eighteenth Text REtrieval Conference, TREC 2009, Gaithersburg, MD, USA (2009)

    Google Scholar 

  7. Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of ACM SIGIR 2008, Singapore, pp. 659–666 (2008)

    Google Scholar 

  8. Cormack, G.V., Clarke, C.L.A., Büttcher, S.: Reciprocal rank fusion outperforms Condorcet and individual rank learning methods. In: Proceedings of ACM SIGIR 2009, Boston, MA, USA, pp. 758–759 (2009)

    Google Scholar 

  9. Van Dang, W., Bruce Croft, W.: Diversity by proportionality: an election-based approach to search result diversification. In: Proceedings of ACM SIGIR 2012, Portland, OR, USA, pp. 65–74 (2012)

    Google Scholar 

  10. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  11. Fox, E.A., Shaw, J.A.: Combination of multiple searches. In: Proceedings of the Second Text REtrieval Conference, TREC 1993, Gaithersburg, MD, USA, pp. 243–252 (1993)

    Google Scholar 

  12. Ghosh, K., Parui, S.K., Majumder, P.: Learning combination weights in data fusion using genetic algorithms. Inf. Process. Manage. 51(3), 306–328 (2015)

    Article  Google Scholar 

  13. Hong, D., Si, L.: Mixture model with multiple centralized retrieval algorithms for result merging in federated search. In: Proceedings of ACM SIGIR 2012, Portland, OR, USA, pp. 821–830 (2012)

    Google Scholar 

  14. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of IJCAI 1995, Montréal, Québec, Canada, pp. 1137–1145 (1995)

    Google Scholar 

  15. Kozorovitzky, A.K., Kurland, O.: Cluster-based fusion of retrieved lists. In: Proceeding of ACM SIGIR 2011, Beijing, China, pp. 893–902 (2011)

    Google Scholar 

  16. Lee, J.H.: Analysis of multiple evidence combination. In: Proceedings of ACM SIGIR 2007, Philadelphia, PA, USA, pp. 267–275 (1997)

    Google Scholar 

  17. Liang, S., Ren, Z., de Rijke, M.: Fusion helps diversification. In: Proceeding of ACM SIGIR 2014, Gold Coast, QLD, Australia, pp. 303–312 (2014)

    Google Scholar 

  18. Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: Proceedings of ACM CIKM Conference, McLean, VA, USA, pp. 538–548 (2002)

    Google Scholar 

  19. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution-A Practical Approach to Global Optimization. Natural Computing. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  20. Rodrygo, L., Santos, T., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of WWW, Raleigh, North Carolina, USA, pp. 881–890 (2010)

    Google Scholar 

  21. Sheldon, D., Shokouhi, M., Szummer, M., Craswell, N.: Lambdamerge: merging the results of query reformulations. In: Proceedings of WSDM, Hong Kong, China, pp. 795–804 (2011)

    Google Scholar 

  22. Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Inf. Retrieval 1(3), 151–173 (1999)

    Article  Google Scholar 

  23. Wu, S., Bi, Y., Zeng, X.: The linear combination data fusion method in information retrieval. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 219–233. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  24. Wu, S., Huang, C.: Search result diversification via data fusion. In: Proceedings of ACM SIGIR 2014, Gold Coast, QLD, Australia, pp. 827–830 (2014)

    Google Scholar 

  25. Wu, S., McClean, S.I.: Performance prediction of data fusion for information retrieval. Inf. Process. Manage. 42(4), 899–915 (2006)

    Article  Google Scholar 

  26. Xia, L., Xu, J., Lan, Y., Guo, J., Cheng, X.: Learning maximal marginal relevance model via directly optimizing diversity evaluation measures. In: Proceedings of ACM SIGIR 2015, Santiago, Chile, pp. 113–122 (2015)

    Google Scholar 

  27. Yue, Y., Joachims, T.: Predicting diverse subsets using structural SVMs. In: Machine Learning, Proceedings of ICML, Helsinki, Finland, pp. 1224–1231 (2008)

    Google Scholar 

  28. Zhu, Y., Lan, Y., Guo, J., Cheng, X., Niu, S.: Learning for search result diversification. In: Proceedings of ACM SIGIR 2014, pp. 293–302 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shengli Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Xu, C., Huang, C., Wu, S. (2016). Differential Evolution-Based Fusion for Results Diversification of Web Search. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39937-9_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics