Automatic text processing
C4.5: programs for machine learning
C4.5: programs for machine learning
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine Learning
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Performance analysis of mobile agents for filtering data streams on wireless networks
Mobile Networks and Applications - Analysis and Design of Multi-Service Wireless Networks
High performance data broadcasting systems
Mobile Networks and Applications
CORBA based design and implementation of universal personal computing
Mobile Networks and Applications
I-WAP: An Intelligent WAP Site Management System
IEEE Transactions on Mobile Computing
Maximizing Text-Mining Performance
IEEE Intelligent Systems
Developing Mobile Wireless Applications
IEEE Internet Computing
A security and usability proposal for mobile electronic commerce
IEEE Communications Magazine
Beyond usability: the OoBE dynamics of mobile data services markets
Personal and Ubiquitous Computing
A novel method for personalized music recommendation
Expert Systems with Applications: An International Journal
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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.