[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2017.22.03.035

MBTI-based Recommendation for Resource Collaboration System in IoT Environment

Park, Jong-Hyun (Dept. of Computer Engineering&Science, Chungnam National University)

Publication Information

Journal of the Korea Society of Computer and Information / v.22, no.3, 2017 , pp. 35-43 More about this Journal

Abstract

In IoT(Internet of Things) environment, users want to receive customized service by users' personal device such as smart watch and pendant. To fulfill this requirement, the mobile device should support a lot of functions. However, the miniaturization of mobile devices is another requirement and has limitation such as tiny display. limited I/O, and less powerful processors. To solve this limitation problem and provide customized service to users, this paper proposes a collaboration system for sharing various computing resources. The paper also proposes the method for reasoning and recommending suitable resources to compose the user-requested service in small device with limited power on expected time. For this goal, our system adopts MBTI(Myers-Briggs Type Indicator) to analyzes user's behavior pattern and recommends personalized resources based on the result of the analyzation. The evaluation in this paper shows that our approach not only reduces recommendation time but also increases user satisfaction with the result of recommendation.

Keywords

MBTI-based Recommendation; Personalized Recommendation; Resource Collaboration; Recommender System; Resource Reasoning;

Citations & Related Records

Reference

1	A. Schmidt, "Ubiquitous Computing: Are We There Yet?", Computer Vol.43, No.2, pp.95-97, February 2010. DOI
2	J. L. Hernandez-Ramos, M. V. Moreno , J. B. Bernabe, D. G. Carrillo and A. F. Skarmeta, "SAFIR: Secure access framework for IoT-enabled services on smart buildings", Journal of Computer and System Sciences, Vol. 81, No 8, pp. 1452-1463, December 2015. DOI
3	J.-H. Park and J.-H. Kang, "Resource collaboration system based on dynamic user preference and context", Artificial Intelligence Review, Vol.34, No.3, pp.271-287, October 2010. DOI
4	J.-H. Park, W.-I. Park, Y.-K. Kim and J.-H. Kang, "A Customized Device Recommender System based on Context-Aware in Ubiquitous Environments" Journal of The Institute of Electronics Engineers of Korea, Vol. 46, CI, No. 3, pp.15-23, May 2009.
5	D. Spoladore, G. Modoni, M. Sacco, "Smart Devices for the Home of the Future: A New Model for Collaboration", ERCIM News, Vol. 2016, No 104, January 2016
6	MC Lee, HK Jang, YS Paik, SE Jin and S Lee, "Device Collaboration Framework in Ubiquitous Environment: Celadon", Proc. of SEUS-WCCIA'06, Gyeongju, Korea, April 2006.
7	S. McFaddin, D. Coffman, J. H. Han, H. K. Jang, J. H. Kim, J. K. Lee, M. C. Lee, Y. S. Moon, C. Narayanaswami, Y. S. Paik, J. W. Park, and D. Soroker, "Celadon: Delivering Business Services to Mobile Users in Public Spaces", IBM Research Report RC24381(W0710-101), October 2007.
8	C. Rana, S. K. Jain, "A study of the dynamic features of recommender systems" Artificial Intelligence Review, Vol.43, No.1, pp.141-153,. January 2012 DOI
9	G. Adomavicius, B. Mobasher, F. Ricci, A. Tuzhilin, "Context-Aware Recommender Systems", Proc. of the 2008 ACM conference on Recommender systems, Lousanne, Switzerland, Oct 2008.
10	Y. Zang, Y. An, X. T. Hu, "Automatically recommending healthy living programs to patients with chronic diseases through hybrid content-based and collaborative filtering", Proc. of BIBM 2014, Belfast, United Kingdom, November 2014.
11	H. Liu, X. Kong, X. Bai, W. Wang, T. M. Bekele, F. Xia, "Context-Based Collaborative Filtering for Citation Recommendation", IEEE Access, Vol 3, pp. 1695-1703, October 2015. DOI
12	A. Saggino, P. Kline, "The Location of the Myers-Briggs Type Indicator in Personality Factor Space", Personality and Individual Differences, Vol. 21, No. 4, pp. 591-597, October 1996. DOI
13	I. Gasmi, H. Seridi-Bouchelaghem, L. Hocine, B. Abdelkarim, "Collaborative filtering recommendation based on dynamic changes of user interest", Intelligent Decision Technologies, Vol. 9, No. 3, pp. 271-281, September 2015. DOI
14	S. H. Ha, "Digital content recommender on the Internet.", IEEE Intelligent Systems, Vol.21, No.2, pp.70-77, March 2006.
15	M. Zanker, F. Ricci, D. Jannach, and L. Terveen, "Measuring the impact of personalization and recommendation on user behaviour", International Journal of Human-Computer Studies, Vol.68, No.8, pp. 469-471, August 2010. DOI