Situation and social awareness-based personalized recommendation service in pervasive computing environment

Recently, many mobile techniques such as sensor networks or various types of mobile devices make it possible to provide smart services at any time, and anywhere. In despite of these remarkable advances of techniques, there are few personalized mobile recommendation services which fully consider user's current situation. Proposed recommendation algorithm efficiently defines user's current situation with situational data captured from various smartphone sensors. Also, the algorithm uses user's social network for efficiently filtering valuable items which are considered as authorities. To verify the usefulness of proposed technique, we implement a prototype of the personalized music recommendation service in which proposed recommendation technique is applied. Additionally, through the demonstration of implemented prototype, we investigate the effect of incorporating smartphone sensor data and social data to collaborative filtering algorithms.