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To achieve the full potential of mobile commerce (m-Commerce), many problems need to be resolved, such as how to make m-Commerce seamlessly span wide areas with heterogeneous information sources; how to improve the matchmaking between user requirements and product specifications; and how to make m-Commerce systems more intelligent in taking different actions according to different user context environments. We believe that the semantic web is a promising technology to solve these problems. In this paper, a framework is proposed in which semantic web ontology is used to model the contexts, user profiles, and product/service information. The semantic web service ontology, OWL-S, is extended for matching user requirements with product specifications at the semantic level, with context information taken into account. Semantic Web Rule Language (SWRL) is used for inferencing with context and user profile descriptions. Our system adopts a multi-agent infrastructure to deal with the integration and interaction between information sources and users. This paper demonstrates some of the great potential of semantic web technology for m-Commerce.

Providing personalized services for mobile commerce (m-commerce) can improve user satisfaction and merchant profits, which are important to the success of m-commerce. This paper proposes a Bayesian network (BN)-based framework for personalization in m-commerce applications. The framework helps to identify the target mobile users and to deliver relevant information to them at the right time and in the right way. Under the framework, a personalization model is generated using a new method and the model is implemented in an m-commerce application for the food industry. The new method is based on function dependencies of a relational database and rough set operations. The framework can be applied to other industries such as movies, CDs, books, hotel booking, flight booking, and all manner of shopping settings.