Usage Data in Web Search: Benefits and Limitations

  • Ricardo Baeza-Yates
  • Yoelle Maarek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7338)

Abstract

Web Search, which takes its root in the mature field of information retrieval, evolved tremendously over the last 20 years. The field encountered its first revolution when it started to deal with huge amounts of Web pages. Then, a major step was accomplished when engines started to consider the structure of the Web graph and link analysis became a differentiator in both crawling and ranking. Finally, a more discrete, but not less critical step, was made when search engines started to monitor and mine the numerous (mostly implicit) signals provided by users while interacting with the search engine. We focus here on this third “revolution” of large scale usage data. We detail the different shapes it takes, illustrating its benefits through a review of some winning search features that could not have been possible without it. We also discuss its limitations and how in some cases it even conflicts with some natural users’ aspirations such as personalization and privacy. We conclude by discussing how some of these conflicts can be circumvented by using adequate aggregation principles to create “ad hoc”crowds.

Keywords

Web search usage data wisdom of crowds large scale data mining privacy personalization long tail 

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References

  1. 1.
    Agarwal, D., Chen, B.C., Elango, P.: Explore/Exploit Schemes for Web Content Optimization. In: Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, pp. 1–10. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R., Broder, A., Maarek, Y.: The New Frontier of Web Search Technology: Seven Challenges, ch. 2, pp. 11–23. Springer (2011)Google Scholar
  3. 3.
    Baeza-Yates, R., Maarek, Y.: Web retrieval. In: Baeza-Yates, R., Ribeiro-Neto, B. (eds.) Modern Information Retrieval: The Concepts and Technology behind Search, 2nd edn. Addison-Wesley (2011)Google Scholar
  4. 4.
    Baeza-Yates, R., Saint-Jean, F.: A Three Level Search Engine Index Based in Query Log Distribution. In: Nascimento, M.A., de Moura, E.S., Oliveira, A.L. (eds.) SPIRE 2003. LNCS, vol. 2857, pp. 56–65. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Barbaro, M., Zeller Jr., T.: A face is exposed for aol searcher no. 4417749. The New York Times, August 9 (2006)Google Scholar
  6. 6.
    Bilton, N.: Erasing the digital past. The New York Times (April 2011), http://www.nytimes.com/2011/04/03/fashion/03reputation.html
  7. 7.
    Brenes, D.J., Gayo-Avello, D., Pérez-González, K.: Survey and evaluation of query intent detection methods. In: Proceedings of the 2009 Workshop on Web Search Click Data, WSCD 2009, pp. 1–7. ACM, New York (2009)CrossRefGoogle Scholar
  8. 8.
    Cutrell, E., Guan, Z.: What are you looking for?: an eye-tracking study of information usage in web search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2007, pp. 407–416. ACM, New York (2007)CrossRefGoogle Scholar
  9. 9.
    Feild, H.A., Allan, J., Jones, R.: Predicting searcher frustration. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 34–41. ACM, New York (2010)CrossRefGoogle Scholar
  10. 10.
    Goel, S., Broder, A., Gabrilovich, E., Pang, B.: Anatomy of the long tail: ordinary people with extraordinary tastes. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 201–210. ACM, New York (2010)CrossRefGoogle Scholar
  11. 11.
    Guo, Q., Agichtein, E.: Exploring mouse movements for inferring query intent. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 707–708. ACM, New York (2008)CrossRefGoogle Scholar
  12. 12.
    Hamilton, A.: Why cuil is no threat to google. Time.com (Time Magazine Online) (July 2008), http://www.time.com/time/business/article/0,8599,1827331,00.html
  13. 13.
    Huang, J., White, R.W., Dumais, S.: No clicks, no problem: using cursor movements to understand and improve search. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, CHI 2011, pp. 1225–1234. ACM, New York (2011)Google Scholar
  14. 14.
    Kadouch, D.: Local flavor for google suggest. The Official Google Blog (March 2009), http://googleblog.blogspot.com/2009/03/local-flavor-for-google-suggest.html
  15. 15.
    Kukich, K.: Techniques for automatically corecting words in text. ACM Computing Surveys 24(4) (December 1992)Google Scholar
  16. 16.
    Mullin, J.: FTC commissioner: If companies don’t protect privacy, we’ll go to congress. paidContent.org, the Economics of Digital Content (February 2011)Google Scholar
  17. 17.
    Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You. Penguin Press (2011)Google Scholar
  18. 18.
    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, SIGIR 2006, pp. 691–692. ACM, New York (2006)CrossRefGoogle Scholar
  19. 19.
    Shi, X.: Social network analysis of web search engine query logs. Technical report, School of Information, University of Michigan (2007)Google Scholar
  20. 20.
    Srikant, R., Basu, S., Wang, N., Pregibon, D.: User browsing models: relevance versus examination. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 223–232. ACM, New York (2010)CrossRefGoogle Scholar
  21. 21.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. Newsl. 1, 12–23 (2000)CrossRefGoogle Scholar
  22. 22.
    Surowiecki, J.: The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Random House (2004)Google Scholar
  23. 23.
    Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2001)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ricardo Baeza-Yates
    • 1
  • Yoelle Maarek
    • 2
  1. 1.Yahoo! ResearchBarcelonaSpain
  2. 2.Yahoo! ResearchHaifaIsrael

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