Cover page and Table of Contents: PDF (size: 392KB)
Full Text (PDF, 392KB), PP.29-35
Views: 0 Downloads: 0
Pervasive Computing, Proactivity, Pervasive Recommender Systems, Case Based Reasoning, Neural Networks
Providing spontaneously personalized services to users, at anytime, anywhere and through any devices represent the main objective of pervasive computing. Smart home is an intelligent environment that can provide dozens or even hundreds of smart services. In this paper, we propose an approach to present spontaneously and continuously the most relevant services to the user in response to any significant change of his context. Our approach allows, firstly to assist proactively the user in the tasks of his/her daily life and secondly to help him/her to save energy in the smart home environment. The proposed approach is based on the use of context history information together with user profiling and machine learning techniques. Experimental results show that our approach can efficiently provide the most useful services to the user in a smart home environment.
Gouttaya Nesrine, Belghini Naouar, Begdouri Ahlame, Zarghili Arslane, "Improving the Proactive Recommendation in Smart Home Environments: An Approach Based on Case Based Reasoning and BP-Neural Network", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.7, pp.29-35, 2015. DOI:10.5815/ijisa.2015.07.04
Weiser M. The computer for the 21st century. On Scientific American, 1991, 265(3): 94-104.
Barlow J, Gann D, Venables T. Digital Futures: Making Homes smarter, 1999, Converty, Publisher: Chartered Institute of Housing book.
Umar S. Architectures for ubiquitous systems. On Technical Report N° 527, 2002, university of Cambridge,
Mozer M C. Lessons from an adaptive home. On Smart Environnements, 2005: 271–294.
Das S, Cook D, Battacharya A, Heierman E, Lin T. The role of prediction algorithms in the MavHome smart home architecture. IEEE Trans. On Wireless Communications, 2002,9(6):77–84.
Gopalratnam K, Cook D J. Online sequential prediction via incremental parsing: The Active LeZi algorithm. IEEE Trans. On Intelligent Systems, 2007,22(1):52–58.
Katharina R. Smart assistants for smart homes. Phd Thesis, 2013,Stockholm, Sweden.
Herlocker J L, Konstan J A. Content-independent task-focused recommendation. IEEE Internet Computing, 2001,5(6) :40–47.
Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, July 1998, Madison, WI.
Melville P, Mooney R, Nagarajan R. Content-boosted collaborative filtering for improved recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Alberta, 2002: 187–192.
Dey A K, Abowd G D. Towards a better understanding of context and context-awareness. Proceedings of the Workshop on the What, Who, Where, When and How of Context-Awareness. 2000, ACM Press, New York .
Aamodt A, Plaza E. Case-Based Reasoning: Foundational Issues, Methodological Variations, and Systems Approaches. On AI Communications Journal,1994, 7(1):39-59.
Tinghuai M, Kim Y, Qiang M, Tang M., Zhou, W. Context-Aware Implementation based on CBR for Smart Home. On Wireless and Mobile Computing, Networking and Communications (WiMob'2005), IEEE International Conference, 2005: 112 – 115.
Leake D, Maguitman A, Reichherzer T. Cases, Context, and Comfort: Opportunities for Case-Based Reasoning in Smart Homes. Lecture Notes in Computer Science, 2006 4008(1):109-131.
Bryson A E, Ho J. Applied optimal control, Blaisdell publishing Co,1969.
Schilit1 B, Adams N, Want R. Context-aware computing applications. IEEE Workshop on Mobile Computing Systems and Applications. 1994, Washington, DC, USA: 85–90.