Mining Context-based User Preferences for m-Services Applications

Human Computer Interaction (HCI) challenges in mobile computing can be addressed by tailoring access and use of mobile services to user preferences. Our investigation of existent approaches to personalisation in context-aware computing found that user preferences are assumed to be static across different context descriptions, whilst in reality some user preferences are transient and vary with the change in context. Furthermore, existent preference models do not give an intuitive interpretation of a preference and lack user expressiveness. To tackle these issues, this paper presents a user preference model and mining framework for a context-aware m-services environment based on an intuitive quantitative preference measure and a strict partial order preference representation. Experimental evaluation of the user preference mining framework in a simulated m-Commerce environment showed that it is very promising. The preference mining algorithms were found to scale well with increases in the volumes of data.