Skip to main content
SpringerLink
Log in

Inferring User Search Intention Based on Situation Analysis of the Physical World

  • Conference paper
Ubiquitous Intelligence and Computing (UIC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6406))

Included in the following conference series:

Abstract

Ubiquitous search of the physical world has recently received significant attention. In this paper, we investigate situation analysis based user intention recognition, which can be useful to retrieve information that may perfectly satisfy a user’s needs. We first introduce a hierarchical user intention model based on CRFs (Conditional Random Fields). With the model, a BP (Belief Propagation) based inference method is proposed to recognize user search intention based on situation analysis. A variety of sensing mechanisms are adopted to collect context information of the physical world for robust and reliable situation analysis. We developed a prototype in a real home setting and experiments were performed to examine the effectiveness of the proposed approach.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Tan, C.C., Sheng, B., Wang, H., Li, Q.: Microsearch: When Search Engines Meet Small Devices. In: Indulska, J., Patterson, D.J., Rodden, T., Ott, M. (eds.) PERVASIVE 2008. LNCS, vol. 5013, pp. 93–110. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Wang, H., Tan, C.C., Li, Q.: Snoogle: A Search Engine for the Physical World. In: The 27th IEEE Conference on Computer Communications, pp. 2056–2064 (2008)

    Google Scholar 

  3. Kiong, Y.K., Vikram, S., Mehul, M.: MAX: Wide Area Human-Centric Search of the Physical World. ACM Transactions on Sensor Networks 4(4), 26 (2008)

    Google Scholar 

  4. Wang, M., Cao, J., Sun, Y., Li, J.: Toward Ubiquitous Searching. In: The ICPADS 2007, pp. 1–8 (2007)

    Google Scholar 

  5. Guo, B., Satake, S., Imai, M.: Home-Explorer: Ontology-based Physical Artifact Search and Hidden Object Detection System. In: Mobile Information Systems, vol. 4, pp. 81–103. IOS Press, Amsterdam (2008)

    Google Scholar 

  6. Frank, C., Bolliger, P., Roduner, C., Kellerer, W.: Objects Calling Home: Locating Objects Using Mobile Phones. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) PERVASIVE 2007. LNCS, vol. 4480, pp. 351–368. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Koolwaaij, J., Tarlano, A., Luther, M., Nurmi, P., Mrohs, B., Battestini, A.: Context Watcher - Sharing Context Information in Everyday Life. In: The International Conference on Web Technologies, Applications, and Services (2006)

    Google Scholar 

  8. Gaonkar, S., Li, J., Choudhury, R.R., Cox, L., Schmidt, A.: Micro-Blog: Sharing and Querying Content through Mobile Phones and Social Participation. In: The 6th International Conference on Mobile Systems, pp. 174–186 (2008)

    Google Scholar 

  9. Dearman, D., Kellar, M., Truong, K.N.: An Examination of Daily Information Needs and Sharing Opportunities. In: The 21th International Conference on Computer Supported Cooperative Work, pp. 679–688 (2008)

    Google Scholar 

  10. Kang, I.-H., Kim, G.C.: Query Type Classification for Web Document Retrieval. In: 26th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 64–71. ACM Press, New York (2003)

    Google Scholar 

  11. Jansen, B.J., Booth, D.L., Spink, A.: Determining the User Intent of Web Search Engine Queries. In: The 16th International Conference on World Wide Web, pp. 1149–1150. ACM Press, New York (2007)

    Chapter  Google Scholar 

  12. Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing Search via Automated Analysis of Interests and Activities. In: 28th ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 449–456. ACM Press, New York (2005)

    Google Scholar 

  13. Baeza-Yates, R., Calderón-Benavides, L., González-Caro, C.: The Intention behind Web Queries. In: Crestani, F., Ferragina, P., Sanderson, M. (eds.) SPIRE 2006. LNCS, vol. 4209, pp. 98–109. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Stamou, S., Ntoulas, A.: Search Personalization Through Query and Page Topical Analysis. In: User Modeling and User-Adapted Interaction, vol. 19(1-2), pp. 5–33. Kluwer Academic Publishers, Hingham (2009)

    Google Scholar 

  15. Park, G., Chae, J., Lee, D.H., Lee, S.: User Intention based Personalized Search: HPS(Hierarchical Phrase Serch). WSEAS Transactions on Circuits and Systems 7(4), 266–276 (2008)

    Google Scholar 

  16. Feng, G., Malek, A., Naphtali, R.: Personalized Approach for Mobile Search. In: The 2009 World Congress on Computer Science and Information Engineering, pp. 322–326. IEEE Computer Society, Washington (2009)

    Google Scholar 

  17. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In: The 14th Conference on Uncertainty in Artificial Intelligence, pp. 256–265 (1998)

    Google Scholar 

  18. Chen, Z., Lin, F., Liu, H., Liu, Y., Ma, W., Liu, W.: User Intention Modeling in Web Applications Using Data Mining. In: The World Wide Web: Internet and Web Information Systems, vol. 5(3), pp. 181–191. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  19. Armentano, M.G., Amandi, A.A.: Recognition of User Intentions for Interface Agents with Variable Order Markov Models. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 173–184. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  20. Burghardt, C., Propp, S., Kirste, T., Forbrig, P.: Rapid Prototyping and Evaluation of Intention Analysis for Smart Environments. In: Tavanganan, D., Kirste, T., Timmermann, D., Lucke, U., Versick, D. (eds.) IMC 2009. CCIS, vol. 53, pp. 239–250. Springer, Heidelberg (2009)

    Google Scholar 

  21. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: The 18th International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  22. Vail, D., Veloso, M., Lafferty, J.: Conditional Random Fields for Activity Recognition. In: The 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1331–1338. ACM Press, New York (2007)

    Google Scholar 

  23. McCallum, A.: Efficiently Inducing Features of Conditional Random Fields. In: The 19th Conference on Uncertainty in Artificial Intelligence, UAI (2003)

    Google Scholar 

  24. Liao, L., Fox, D., Kautz, H.: Hierarchical Conditional Random Fields for GPS-Based Activity Recognition. Robotics Research 28, 478–506 (2007)

    MATH  Google Scholar 

  25. Yedidia, J., Freeman, W., Weiss, Y.: Understanding Belief Propagation and Its Generalizations: Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, P.R. China

    Zhu Wang, Xingshe Zhou, Zhiwen Yu & Yanbin He

  2. Institut TELECOM & Management SudParis, France

    Daqing Zhang

Authors
  1. Zhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingshe Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiwen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanbin He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, 710072, Xi’an, China

    Zhiwen Yu  & Xingshe Zhou  & 

  2. Institute ofTechnology, Faculty of Engineering and Applied Science, University of Ontario, L1H 7K4, Oshawa, ON, Canada

    Ramiro Liscano

  3. Department of Computer Science, University of Massachusetts Lowell, 01854, Lowell, MA, USA

    Guanling Chen

  4. Telecommunication Network and Services Department, Institute TELECOM SudParis, 9 rue Charles Fourier, 91011, Evry Cedex, France

    Daqing Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, Z., Zhou, X., Yu, Z., He, Y., Zhang, D. (2010). Inferring User Search Intention Based on Situation Analysis of the Physical World. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds) Ubiquitous Intelligence and Computing. UIC 2010. Lecture Notes in Computer Science, vol 6406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16355-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16355-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16354-8

  • Online ISBN: 978-3-642-16355-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation