A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
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.
KeywordsPervasive computing Unobtrusive applications Service recommendation
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- 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.
- 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
- 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
- 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.
- 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.Norman, S., Fabien, G., & Kwon, O. B. (2006). Ambient intelligence and pervasive computing, chap. Ambient Intelligence: The MyCampus Experience. ArTech House.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.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.Google Scholar
- 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.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
- 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.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.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.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.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