Abstract
Mobile data communication generated 10 percent of the overall data revenue in Germany and between 2–3 percent worldwide [29] in 2004. This development is contradictory compared to the high investment (91.5 billion €) in networks and licenses that supported the UMTS infrastructure and thereby the mobile internet. An advertised-based revenue model [10] addresses an opportunity to increase the mobile data communication. Mobile customers and advertisers are matched based on the customer’s current situation (location, time and interests). Precise customer profiles, as a requirement to overcome the information overflow, and to enable a multilateral economically reasonable matching are indispensable but the profile’s quality is not given in reality. Without precise customer profiles there is no matching. With situation adaptive customer profiles the profile’s quality is increasing. Its design, realization and integration into the mobile operator’s infrastructure are the aim of this paper.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
1. A. Albers, S. Figge and M. Radmacher, LOC3-Architecture Proposal for Efficient Subscriber Localisation in Mobile Commerce Infrastructures, Proceedings of 2nd IEEE International Workshop on Mobile Commerce and Service, Munich, Germany, 2005.
2. G. Amato and U. Straccia, User Profile Modelling and Applications to Digital Libraries, ECDL’ 99, Abiteboul, S., Vercoustre, A.-M.(Eds.), 1999, pp. 184–197.
3. M. Balabanović, and Y. Shoham, Fab: content-based, collaborative recommendation, Communication of the ACM, March 1997/Vol. 40, No. 3.
4. R. Bulander, M. Decker, B. Kölmel and G. Schiefer, Kontextsensitives mobiles Marketing, in: B. König-Ries, M. Klein (Hrsg.): Mobile Datenbanken und Informationssysteme, in Business, Technologie und Web. BTW 2005, Universität Karlsruhe 2005, pp. 11–20.
5. Bundesnetzagentur, 2006, Jahresbericht 2006. Bundesnetzagentur, Bonn.
6. R. Carreira, J.M. Crato, D. Gonçalves and J.A. Jorge, Evaluating Adaptive User Profiles for News Classification, 2004.
7. U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, Knowledge Discovery and Data Mining Towards a Unifying Framework, 1996.
8. S. Figge, Situation-dependent services—a challenge for mobile network operators, Journal of Business Research, Volume 57, Issue 12, 2004, pp. 1416–1422.
9. S. Figge and A. Albers, Individualising M-Commerce Services by Semantic User Situation Modelling, Proceedings of the 7th International Conference Wirtschaftsinformatik, Bamberg, 2005.
10. S. Figge and S. Theysohn, Quantifizierung IKS-basierter Marktleistungen-Analyse eines werbefinanzierten Geschäftsmodells für den Mobile Commerce, Wirtschafsinformatik 48(2), 2006, pp. 96–106.
11. W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus, Knowledge Discovery in Databases: An Overview. AAAI, 1992, pp. 56–70.
12. M. Goel, S. Sarkar, Web Site Personalization Using User Profile Information, AH2002, P. De Bra, P. Brusilovsky and R. Conejo, (Eds.), 2002, pp. 510–513.
13. D. Goren-Bar and O. Glinansky, FIT-recommending TV programs to family members. Computer & Graphics, 28, 2004, pp.149–156.
14. A.R. Hevner, S.T. March, J. Park and S. Ram, Design Science Information Systems Research. MIS Quarterly, Vol. 28 No. 1, 2004, pp. 75–105.
15. C. Kaspar, O. von Wersch, I. Hochstatter, A. Albers, S. Figge, et al., Mobile Anwendungen-Best Practices in der TIME-Branche. Hrsg.: T. Hess, S. Hagenhoff, D. Hogrefe, C. Linnhoff-Popien, K. Rannenberg and F. Straube, Universitätsverlag Göttingen, 2005.
16. R.R. Korfnage, Query Enhancement by User Profiles, Joint BCS & ACM Symposium on Research & Development in Information Retrieval, 1984, pp.111–121.
17. R. Kraemer and P. Schwander, Bluetooth based wireless Internet applications for indoor hot spots: experience, 2000.
18. R.J. Lukose, E. Adar, J.R. Tyler and C. Sengupta, SHOCK: Communicating with Computational messages and Automatic Private Profiles, Proceedings of WWW 2003, May 20–24, Budapest, 2003, pp. 291–300.
19. B. Salem and M. Rauterberg, Multiple User Profile Merging (MUPE): Key Challenges for Environment Awareness, EUSAI 2004, pp. 196–206.
20. J. Schackmann, Ökonomische vorteilhafte Individualisierung und Personalisierung, 2002
21. M. Schuhmann, Individualität und Produktindividualisierung — Kundenprofile für die Personalisierung von Produkten, 2004.
22. C. Shapiro and H.R. Varian, Information Rules, 1999.
23. S. Singh, M. Shepherd, J. Duffy and C. Watters, An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy, 2006.
24. U. Spitzer, Recommender Systeme im E-Commerce. Wirtschaftsuniversität Wien, 2005.
25. K. Sugiyama, K. Hatano and M. Yoshikawa, Adaptive Web Search Based on User Profile Constructed without Any Effort from Users, 2005.
26. P. Srinil, and O. Pinngern, Adaptive User Profile for Information Retrieval from the Web, 2002.
27. H. Takeda, P. Veerkamp, T. Tomiyama and H. Yoshikawam, Modeling Design Processes, AI Magazine, 1990, pp. 37–48.
28. V. Vaishnavi and B. Kuechler, Design Research in Information Systems, AIS 2006.
29. Vodafone Group Plc, Key Performance Indicators, 2006, Available:
http://www.vodafone.com/assets/files/en/VOD_KPIs_20041231_2.xls, Abruf am 2005-11-01
30. Y.Z. Wei, L. Moreau and N.R. Jennings, A Market-Based Approach to Recommender Systems, ACM Transactions on Information Systems (TOIS), 2005, pp. 227–266.
31. P. Zipkin, The limits of mass customization, Sloan Management Review 42,3, 2001, pp. 81–87.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 International Federation for Information Processing
About this paper
Cite this paper
Radmacher, M. (2007). Adaptive Customer Profiles For Context Aware Services in a Mobile Environment. In: Wang, W., Li, Y., Duan, Z., Yan, L., Li, H., Yang, X. (eds) Integration and Innovation Orient to E-Society Volume 1. IFIP — The International Federation for Information Processing, vol 251. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75466-6_44
Download citation
DOI: https://doi.org/10.1007/978-0-387-75466-6_44
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-75465-9
Online ISBN: 978-0-387-75466-6
eBook Packages: Computer ScienceComputer Science (R0)