Mobile content recommendation system for re-visiting user using content-based filtering and client-side user profile

Mobile content recommendation system has been widely used to overcome limitation of usability on mobile device for mobile content filtering problem. However, the system faces the problem related to insufficient information in the early stage. This affects the performance of the system in the prediction of mobile content for relevant items for user. There is a Multi-level Targeting Classification Association Rule (MTCAR) technique that addresses the problem by building an integrated model for mobile content recommendation systems. However, a revisiting user is already associated with certain past history. Therefore, an improved MTCAR technique is proposed to address the problem. In this research MTCAR with content-based filtering and user profile aims to provide better recommendations to revisiting users compared to conventional MTCAR. Users can manage their preferences and profiles, in order to obtain the personalized mobile content thereby improving the overall performance.