Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, “Relevance” is determined based on a user-centric approach that combines both the reputation of the service, the user’s current context, the user’s profile, as well as a record of the history of recommendations. Our decision making mechanism is adaptive in the sense that it is able to cope with users’ contexts that are changing and drifts in the users’ interests, while it simultaneously can track the reputations of services, and suppress repetitive notifications based on the history of the recommendations. The paper also includes some brief but comprehensive results concerning the task of tracking service reputations by analyzing and comprehending Word-of-Mouth communications, as well as by suppressing repetitive notifications. We believe that our architecture presents a significant contribution towards realizing intelligent and personalized service provisioning in pervasive environments.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Aghasaryan A., Betgé-Brezetz S., Senot C., Toms Y. (2008) A profiling engine for converged service delivery platforms. Bell Labs Technical Journal 13(2): 93–103

    Article  Google Scholar 

  2. Aguilera, M. K., Strom, R. E., Sturman, D. C., Astley, M., & Chandra, T. D. (1999). Matching events in a content-based subscription system. In PODC ’99: Proceedings of the Eighteenth Annual ACM Symposium on Principles of Distributed Computing ACM, New York, NY, USA (pp. 53–61). doi:10.1145/301308.301326.

  3. Arbanowski S., Ballon P., David K., Droegehorn O., Eertink H., Kellerer W. et al (2004) I-centric communications: Personalization, ambient awareness, and adaptability for future mobile services. IEEE Communications Magazine 42(9): 63–69

    Article  Google Scholar 

  4. Brunato, M., & Battiti, R. (2003). Pilgrim: A location broker and mobility-aware recommendation system. In PERCOM ’03: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (p. 265). Washington, DC, USA: IEEE Computer Society.

  5. Dey A. K. (2001) Understanding and using context. Personal and Ubiquitous Computing 5(1): 4–7. doi:10.1007/s007790170019

    Article  Google Scholar 

  6. Eugster P. T., Felber P. A., Guerraoui R., Kermarrec A. M. (2003) The many faces of publish/ subscribe. ACM Computing Surveys 35: 114–131

    Article  Google Scholar 

  7. Garlan D., Siewiorek D., Smailagic A., Steenkiste P. (2002) Project aura: Toward distraction-free pervasive computing. IEEE Pervasive Computing 1(2): 22–31. doi:10.1109/MPRV.2002.1012334

    Article  Google Scholar 

  8. Hinze, A., & Voisard, A. (2003). Location- and time-based information delivery in tourism. In Proceedings of 8th International Symposium in Spatial and Temporal Databases (SSTD) (pp. 489–507). Springer.

  9. Hossain M. A., Atrey P. K., El Saddik A. (2008) Gain-based selection of ambient media services in pervasive environments. Mobile Networks and Applications 13(6): 599–613

    Article  Google Scholar 

  10. Hossain M. A., Parra J., Atrey P. K., El Saddik A. (2009) A framework for human-centered provisioning of ambient media services. Multimedia Tools and Applications 44: 407–431

    Article  Google Scholar 

  11. Kaasinen E. (2003) User needs for location-aware mobile services. Personal Ubiquitous Computing 7(1): 70–79. doi:10.1007/s00779-002-0214-7

    Article  Google Scholar 

  12. Khedo, K. K. (2006). Context-aware systems for mobile and ubiquitous networks. In ICNICONSMCL ’06: Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (p. 123). Washington, DC, USA: IEEE Computer Society. doi:10.1109/ICNICONSMCL.2006.68.

  13. Kurkovsky S., Harihar K. (2006) Using ubiquitous computing in interactive mobile marketing. Personal Ubiquitous Computing 10(4): 227–240. doi:10.1007/s00779-005-0044-5

    Article  Google Scholar 

  14. Maloof M. A., Michalski R. S. (2000) Selecting examples for partial memory learning. Machine Learning 41: 27–52

    Article  Google Scholar 

  15. Mitchell T. M., Caruana R., Freitag D., McDermott J., Zabowski D. (1994) Experience with a learning personal assistant. Communications of the ACM 37(7): 80–91. doi:10.1145/176789.176798

    Article  Google Scholar 

  16. Montaner M., Lopez B., de la Rosa J. L. (2003) A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19: 285–330

    Article  Google Scholar 

  17. Naudet Y., Aghasaryanb A., Mignon S., Toms Y., Senot C. (2010) Ontology-based profiling and recommendations for mobile tv. In: Wallace M., Anagnostopoulos I., Mylonas P., Bielikova M. (eds) Semantics in adaptive and personalized services, studies in computational intelligence, Vol 279. Springer, Berlin/Heidelberg, pp 23–48

    Chapter  Google Scholar 

  18. Norman, S., Fabien, G., & Kwon, O. B. (2006). Ambient intelligence and pervasive computing, chap. Ambient Intelligence: The MyCampus Experience. ArTech House.

  19. Oommen B. J., Rueda L. (2006) Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments. Pattern Recognition 39(3): 328–341. doi:10.1016/j.patcog.2005.09.007

    Article  MATH  Google Scholar 

  20. Riva, O. (2006). Contory: A middleware for the provisioning of context information on smart phones. In Middleware ’06: Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware, (pp. 219–239). New York, Inc., New York, NY, USA: Springer.

  21. Riva O., Toivonen S. (2007) The dynamos approach to support context-aware service provisioning in mobile environments. Journal of Systems and Software 80(12): 1956–1972. doi:10.1016/j.jss.2007.03.009

    Article  Google Scholar 

  22. Schlosser, A., Voss, M., & BrÄuckner, L. (2005). On the simulation of global reputation systems. Journal of Artificial Societies and Social Simulation 9(1), 4.

    Google Scholar 

  23. Schmidt, A., Aidoo, K. A., Takaluoma, A., Tuomela, U., Laerhoven, K.V., & Velde, W. V. D. (1999). Advanced interaction in context. In HUC ’99: Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing (pp. 89–101). London, UK: Springer.

  24. Schwab, I., Kobsa, A., & Koychev, I. (2001). Learning user interests through positive examples using content analysis and collaborative filtering. In 30 2001. Internal Memo, GMD.

  25. Sutterer, M., Droegehorn, O., & David, K. (2007). User profile management on service platforms for ubiquitous computing environments. In VTC Spring (pp. 287–291).

  26. Weiser M. (1991) The computer for the twenty-first century. Scientific American 265(3): 94–104

    Article  Google Scholar 

  27. Widmer G. (1997) Tracking context changes through meta-learning. Machine Learning 27(3): 259–286. doi:10.1023/A:1007365809034

    Article  Google Scholar 

  28. Yang W. S., Cheng H. C., Dia J. B. (2008) A location-aware recommender system for mobile shopping environments. Expert Systems with Applications 34(1): 437–445. doi:10.1016/j.eswa.2006.09.033

    Article  Google Scholar 

  29. Yazidi, A. (2011). Intelligent learning automata-based strategies applied to personalized service provisioning in pervasive environments. Ph.D. thesis, Department of ICT, University of Agder, Grimstad, Norway.

  30. Yazidi, A., Granmo, O. C., Lin, M., Wen, X., Oommen, B. J., Gerdes, M., et al. (2010). Learning automaton based on-line discovery and tracking of spatio-temporal event patterns. In B. T. Zhang & M. Orgun (Eds.), PRICAI 2010: Trends in artificial intelligence, Lecture notes in computer science Vol. 6230 (pp 327–338). Berlin/Heidelberg: Springer.

  31. Yazidi, A., Granmo, O. C., & Oommen, B. J. An adaptive approach to learning the preferences of users in a social network using weak estimators. Submitted for publication.

  32. Yazidi, A., Granmo, O. C., & Oommen, B. J. Service selection in stochastic environments: A learning-automaton based solution. To Appear in Applied Intelligence.

  33. Yazidi, A., Granmo, O. C., & Oommen, B. J. (2010). A learning automata based solution to service selection in stochastic environments. In Proceedings of the Twenty Second International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems (IEA-AIE 2010), Lecture Notes in Artificial Intelligence (pp. 209–218).

  34. Yu Z., Zhou X., Zhang D., Chin C. Y., Wang X., Men J. (2006) Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing 5: 68–75. doi:10.1109/MPRV.2006.61

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ICT, University of Agder, Grimstad, Norway

    Anis Yazidi & Ole-Christoffer Granmo

  2. School of Computer Science, Carleton University, Ottawa, ON, K1S 5B6, Canada

    B. John Oommen

  3. University of Agder, Grimstad, Norway

    B. John Oommen & Frank Reichert

  4. Ericsson Research, Aachen, Germany

    Martin Gerdes

Authors
  1. Anis Yazidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ole-Christoffer Granmo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. John Oommen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Gerdes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frank Reichert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anis Yazidi.

Additional information

The first author gratefully acknowledges the financial support of the Ericsson Research, Aachen, Germany, and the third author is grateful for the partial support provided by NSERC, the Natural Sciences and Engineering Research Council of Canada.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yazidi, A., Granmo, OC., Oommen, B.J. et al. A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments. Wireless Pers Commun 61, 543–566 (2011). https://doi.org/10.1007/s11277-011-0387-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-011-0387-3

Keywords

Search

Navigation