Constraint satisfaction and debugging for interactive user interfaces
Constraint satisfaction and debugging for interactive user interfaces
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Data mining: concepts and techniques
Data mining: concepts and techniques
User Modeling and User-Adapted Interaction
NAPA: Nearest Available Parking Lot Application
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Proactive Caching for Spatial Queries in Mobile Environments
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Location-Dependent Queries in Mobile Contexts: Distributed Processing Using Mobile Agents
IEEE Transactions on Mobile Computing
Location-Based Spatial Query Processing in Wireless Broadcast Environments
IEEE Transactions on Mobile Computing
Continuous Monitoring of Spatial Queries in Wireless Broadcast Environments
IEEE Transactions on Mobile Computing
Mobile services: trends and evolution
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 2
The demonstration research of mobile value-added services in undergraduate
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Interactive ontology-based user knowledge acquisition: a case study
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Improving explicit profile acquisition by means of adaptive natural language dialog
UM'05 Proceedings of the 10th international conference on User Modeling
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With ample options of service providers available today in mobile communications; it is necessary for the service providers (SP)to retain their customer base by enhancing user experience. We believe that personalization and prediction of user needs is a way to achieve it. Currently the service providers use push based policy for advertising and announcing services; which may or may not be of use to the user. This paper discusses an algorithm that personalizes the Mobile Value Added Services (MVAS) for each subscriber. The paper describes a model to analyze user attributes and personalize various services offered to the user on a mobile phone. The model uses a learning rank algorithm that determines a set of services customized according to the user's preferences. It also uses summarized user profile stored at the VLR (Visitor location Register), which helps in reducing overheads. The model is self learning model that is trained to anticipate the users need and preferences thereby refining it.