A Context-Aware Recommender System for M-Commerce Applications

M-commerce is an attractive research area due to its relative novelty, rapid growth, and great potential in business applications. However, the development of M-commerce applications is facing with some physical constraints of mobile devices and barriers of existing execution models. Moreover, the nomadic users might consume enormous time to search for satisfactory products or services from abundant options with the limited capability of physical devices. Therefore, a sophisticated recommendation algorithm which attempts to recommend a list of user-preferred products or services should be incorporated in M-commerce applications. In this paper, we propose a personalized Context-aware M-commerce Recommender System which exploits the advantages of collaborative filtering and common understanding of contextual information. Since the recommendation algorithm is embedded in a layered system and closed related with other system components, we will present a comprehensive framework to integrate the concepts of mobile agent, ontology-based context model as well as service discovery and selection mechanism. We have developed a prototype to evaluate the feasibility and effectiveness of our proposal.