Abstract
Most of recommendation mechanisms have been attempting to identify a set of cohesive user groups (or clusters) of which members in the same group might be interested in a common area (e.g., movies and musics) than others. Practically, this assumption is not working well, because the statistical analysis to extract simple demographic features (e.g., ages, genders, and so on) can not find out personal context in a certain situation, i.e., a more specific recommendation for each person. In order to solve this problem, we want to take into account social relations (particularly, kin relations) to identify each person. As aggregating the social networks, we can build a social network for making various social relations extractable. Most importantly, we are introducing our experiences on discovering social networks for providing personalized mobile services. Real customer information has been provided from KT Freetel (KTF), one of the major telecommunication companies in Korea. This work is an on-going research project for delivering personalized information to mobile devices via the social networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87 (1997)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: CSCW 2000: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp. 241–250. ACM, New York (2000)
Bonhard, P., Harries, C., McCarthy, J., Sasse, M.A.: Accounting for taste: using profile similarity to improve recommender systems. In: CHI 2006: Proceedings of the SIGCHI conference on Human Factors in computing systems, pp. 1057–1066. ACM, New York (2006)
Kazienko, P.: Expansion of telecommunication social networks. In: Luo, Y. (ed.) CDVE 2007. LNCS, vol. 4674, pp. 404–412. Springer, Heidelberg (2007)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)
Petit-Rozé, C., Strugeon, E.G.L.: MAPIS, a multi-agent system for information personalization. Information & Software Technology 48(2), 107–120 (2006)
Changchien, S.W., Lee, C.F., Hsu, Y.J.: On-line personalized sales promotion in electronic commerce. Expert Systems with Applications 27(1), 35–52 (2004)
Kwon, O., Choi, S., Park, G.: NAMA: a context-aware multi-agent based web service approach to proactive need identification for personalized reminder systems. Expert Systems with Applications 29(1), 17–32 (2005)
Jung, J.J.: Visualizing recommendation flow on social network. Journal of Universal Computer Science 11(11), 1780–1791 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jung, J.J., Kim, K., Lee, H., Park, S. (2008). Are You Satisfied with Your Recommendation Service?: Discovering Social Networks for Personalized Mobile Services. In: Nguyen, N.T., Jo, G.S., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science(), vol 4953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78582-8_57
Download citation
DOI: https://doi.org/10.1007/978-3-540-78582-8_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78581-1
Online ISBN: 978-3-540-78582-8
eBook Packages: Computer ScienceComputer Science (R0)