Abstract
In addition to voice transmission over mobile networks, the demand of data communication has been increasing. To deploy data-oriented applications for mobile terminals, the wireless application protocol (WAP) has provided a promising solution. However, as in the World Wide Web (WWW), the increasing information leads to the problem of information overload. One way to overcome such a problem is to build intelligent recommender systems to provide customised information services. By analyzing the information collected from the user, a customised recommender system is able to reason his personal preferences and to build a model of predictions. In this way, only the information predicted as user-interested can reach the end user. This paper presents a multi-agent framework in which a decision tree-based approach is employed to learn a user’s preferences. To assess the proposed framework, a mobile phone simulator is used to represent a mobile environment and a series of experiments are conducted. The experimental studies have concentrated on how to recommend appropriate information to the individual user, and on how the system can adapt to a user’s most recent preferences. The results and analysis show that based on our framework the WAP-based customised information services can be successfully performed.
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References
Read K, Maurer F (2003) Developing mobile wireless applications. IEEE Inter Comput 7(1):81 –86
Törö M, Huynh T, Zhu J, Liu K and Leung VCM (2003) CORBA based design and implementation of universal personal computing. Mob Netwks App 8(1):75–86
Soriano M, Ponce D (2002) A security and usability proposal for mobile electronic commerce. IEEE Comm Mag 40(8):62 –67
Riikonen P, Boberg J, Salakoski T and Vihinen M (2002) Mobile access to biological databases on the Internet. IEEE Trans Biomed Engin 49(12):1477–1479
Mohan M (2000) Voice enabled request and response for mobile devices supporting WAP protocol. In: Proceedings of the IEEE International Conference on Vehicular Technology, Boston, MA, September 2000
Hwang GJ, Tseng JCR and Huang YS (2002) I-WAP: an intelligent WAP site management system. IEEE Trans Mob Comput 1(2):82–95
Triantafillou P, Harpantidou R and Paterakis M (2002) High performance data broadcasting systems. Mob Netwks App 7(4):279–290
Kotz D, Cybenko G, Gray RS, Jiang G, Peterson RA, Hofmann MO, Chacon DA, Whitebread KR and Hendler J (2002) Performance analysis of mobile agents for filtering data streams on wireless networks. Mob Netwks App 7(3):163–174
Salton G (1989) Automatic text processing. Addison-Wesley, Reading, MA
Balabanovic M, Shoham Y (1997) Fab: content-based collaborative recommendation. Comm ACM 40(3):66–72
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of the ACM International Conference on Digital Libraries, San Antonio, TX, 4–7 June 2000
Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, Denver, CO, 7–11 May 1995
Basu C, Hirsh H and Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the National Conference on Artificial Intelligence, Madison, WI, 26–30 July 1998
Smyth B, Cotter P (2000) A personalised TV listings service for the digital TV age. Know Bas Sys 13:53–59
Lee W-P, Liu C-H and Lu CC (2002) Intelligent agent-based systems for personalized recommendations in Internet commerce. Exp Sys App 22(4):275–284
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kauffmann, San Mateo, CA
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Weiss SM, Apte C, Dameran FJ, Johnson DE, Oles FJ, Goetz T and Hampp T (1999) Maximizing text-mining performance. IEEE Intellig Sys 14(4):63–69
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Lee, WP., Lu, CC. Customising WAP-based information services on mobile networks. Pers Ubiquit Comput 7, 321–330 (2003). https://doi.org/10.1007/s00779-003-0247-6
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DOI: https://doi.org/10.1007/s00779-003-0247-6