Wireless Personal Communications

, Volume 61, Issue 3, pp 543–566 | Cite as

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

  • Anis YazidiEmail author
  • Ole-Christoffer Granmo
  • B. John Oommen
  • Martin Gerdes
  • Frank Reichert


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.


Pervasive computing Unobtrusive applications Service recommendation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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): 93–103CrossRefGoogle Scholar
  2. 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. 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): 63–69CrossRefGoogle Scholar
  4. 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.Google Scholar
  5. 5.
    Dey A. K. (2001) Understanding and using context. Personal and Ubiquitous Computing 5(1): 4–7. doi: 10.1007/s007790170019 CrossRefGoogle Scholar
  6. 6.
    Eugster P. T., Felber P. A., Guerraoui R., Kermarrec A. M. (2003) The many faces of publish/ subscribe. ACM Computing Surveys 35: 114–131CrossRefGoogle Scholar
  7. 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 CrossRefGoogle Scholar
  8. 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.Google Scholar
  9. 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): 599–613CrossRefGoogle Scholar
  10. 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: 407–431CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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. 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 CrossRefGoogle Scholar
  14. 14.
    Maloof M. A., Michalski R. S. (2000) Selecting examples for partial memory learning. Machine Learning 41: 27–52CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 16.
    Montaner M., Lopez B., de la Rosa J. L. (2003) A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19: 285–330CrossRefGoogle Scholar
  17. 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 23–48CrossRefGoogle Scholar
  18. 18.
    Norman, S., Fabien, G., & Kwon, O. B. (2006). Ambient intelligence and pervasive computing, chap. Ambient Intelligence: The MyCampus Experience. ArTech House.Google Scholar
  19. 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 zbMATHCrossRefGoogle Scholar
  20. 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.Google Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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. 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.Google Scholar
  24. 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.Google Scholar
  25. 25.
    Sutterer, M., Droegehorn, O., & David, K. (2007). User profile management on service platforms for ubiquitous computing environments. In VTC Spring (pp. 287–291).Google Scholar
  26. 26.
    Weiser M. (1991) The computer for the twenty-first century. Scientific American 265(3): 94–104CrossRefGoogle Scholar
  27. 27.
    Widmer G. (1997) Tracking context changes through meta-learning. Machine Learning 27(3): 259–286. doi: 10.1023/A:1007365809034 CrossRefGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 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.Google Scholar
  30. 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.Google Scholar
  31. 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.Google Scholar
  32. 32.
    Yazidi, A., Granmo, O. C., & Oommen, B. J. Service selection in stochastic environments: A learning-automaton based solution. To Appear in Applied Intelligence.Google Scholar
  33. 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).Google Scholar
  34. 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

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Anis Yazidi
    • 1
    Email author
  • Ole-Christoffer Granmo
    • 1
  • B. John Oommen
    • 2
    • 3
  • Martin Gerdes
    • 4
  • Frank Reichert
    • 3
  1. 1.Department of ICTUniversity of AgderGrimstadNorway
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada
  3. 3.University of AgderGrimstadNorway
  4. 4.Ericsson ResearchAachenGermany

Personalised recommendations