Context-aware Mobile Recommendation System Based on Context History

Recommendation systems for the mobile Web have focused on endorsing specificr content based on user preferences. But, user preferences vary in different contexts, such as at different times of day and in different locations. Therefore, in a mobile networking setting, providing proactive personalized service is more likely to depend on actual user context. This paper proposed a context-aware mobile recommendation system framework based on user models utilizing the context history. The approach was validated in the tourism domain. From our experiment and evaluation, the proposed framework is a promising approach to provider proactive personalized services to mobile users. Moreover, this research offers the personalized services to new users analyzing between the new user’s information and the stored association rules.