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Modeling Transactional Queries via Templates

  • Edward Bortnikov
  • Pinar Donmez
  • Amit Kagian
  • Ronny Lempel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

Search queries have been roughly classified into three categories – navigational, informational and transactional. The latter group includes queries that aim to perform some Web-mediated task, often by interacting with parameterized Web services. In order to assist users in completing tasks online, one of the first building blocks is identifying whether and which transactional use-case is associated with each query.

This paper describes a framework and an algorithm for automatically generating compact representations of queries associated with transactional use cases. We mine search click logs for queries that lead to clicks on pages associated with a use-case, generalize the set of mined queries into templates by replacing query terms with taxonomy categories, and eliminate redundancies. This approach allows associating the use-case with queries unseen in the log sample, while keeping a concise model. Our methodology allows a business owner to select an appropriate operating point that balances the tradeoff between precision and recall. We report the results of an offline evaluation of our framework on three transactional domains, and demonstrate the viability of the approach.

Keywords

Template Model Query Pattern Anchor Text Query Instance International World Wide 
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

  1. 1.
    Agarwal, G., Kabra, G., Chang, K.C.-C.: Towards rich query interpretation: walking back and forth for mining query templates. In: Proc. 19th International World Wide Web Conference (WWW 2010), pp. 1–10 (2010)Google Scholar
  2. 2.
    Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 1–28 (2002)CrossRefGoogle Scholar
  3. 3.
    Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: OSDI 2004: Sixth Symposium on Operating System Design and Implementation (December 2004)Google Scholar
  4. 4.
    Dong, A., Chang, Y., Zheng, Z., Mishne, G., Bai, J., Zhang, R., Buchner, K., Liao, C., Diaz, F.: Towards recency ranking in web search. In: Proc. 3rd ACM Conference on Web Search and Data Mining, WSDM 2010 (2010)Google Scholar
  5. 5.
    Garey, M.R., Johnson, D.S.: Computers and Intractability, A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  6. 6.
    Herrera, M.R., de Moura, E.S., Cristo, M., Silva, T.P., da Silva, A.S.: Exploring features for the automatic identification of user goals in web search. Information Processing and Management 46(2), 131–142 (2010)CrossRefGoogle Scholar
  7. 7.
    Jansen, B.J., Booth, D.L., Spink, A.: Determining the informational, navigational, and transactional intent of web queries. Inf. Process. Manage. 44, 1251–1266 (2008)CrossRefGoogle Scholar
  8. 8.
    Kang, I.-H.: Transactional Query Identification in Web Search. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.-H. (eds.) AIRS 2005. LNCS, vol. 3689, pp. 221–232. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proc. 14th International World Wide Web Conference (WWW 2005), pp. 391–400 (2005)Google Scholar
  10. 10.
    Li, Y., Krishnamurthy, R., Vaithyanathan, S., Jagadish, H.V.: Getting work done on the web: supporting transactional queries. In: Proc. 29th Annual International ACM SIGIR Conference (SIGIR 2006), pp. 557–564 (2006)Google Scholar
  11. 11.
    Ling, C., Ling, C.X., Gao, J., Qian, W., Zhang, H., Zhang, H.: Mining generalized query patterns from web logs. In: HICSS (2001)Google Scholar
  12. 12.
    Miller, G.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  13. 13.
    Pujeri, R.V., Karthik, G.M.: Constraint based frequent pattern mining for generalized query templates from web log. International Journal of Engineering, Science and Technology 2(11), 17–33 (2010)Google Scholar
  14. 14.
    Szpektor, I., Gionis, A., Maarek, Y.: Improving recommendation for long-tail queries via templates. In: Proc. 20th International World Wide Web Conference, WWW 2011 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Edward Bortnikov
    • 1
  • Pinar Donmez
    • 2
  • Amit Kagian
    • 1
  • Ronny Lempel
    • 1
  1. 1.Yahoo! LabsHaifaIsrael
  2. 2.Yahoo! LabsSunnyvaleIsrael

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