Seven good reasons for mobile agents
Communications of the ACM
User needs for location-aware mobile services
Personal and Ubiquitous Computing
Enabling Technology for Personalizing Mobile Services
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 3 - Volume 3
Learning User Preferences for Wireless Services Provisioning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
An Architecture and Business Model for Making Software Agents Commercially Viable
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03
Context-aware middleware for mobile multimedia applications
Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
Device-aware discovery and ranking of mobile services
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
MobiEureka: an approach for enhancing the discovery of mobile web services
Personal and Ubiquitous Computing
An approach to social recommendation for context-aware mobile services
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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In this paper we present SmartCon, a context-aware system for the discovery and selection of mobile services using Artificial Neural Networks (ANNs). The solution we have developed is a mobile agent-enabled system that adaptively and iteratively learns to select the best available mobile service derived from the extraction of a series of features utilizing contextual information such as the Composite Capabilities/Preferences Profile (CC/PP), service-specific, and non-uniform user-specific features which are supplied to a backpropagation neural network. Based on the features provided, the neural network classifies the most relevant mobile service. In the present work, the system is also capable through iterative learning to generalize and gather information using cognitive feedback based on user's decisions and interactivity with a mobile device. SmartCon is evaluated using a series of preliminary empirical data and results show an 87% success rate in the discovery and selection of the best or most relevant mobile service.