Title: RecMirror: A Multi-Modal Intelligent System for Multimedia Recommendations

Abstract
Traditional recommendation approaches do not consider the changes of the users preferences according to context.  Recommendations Can be adapted to the context of the user using a collaborative filtering approach.  In this project, we propose a designing and developing method to facilitate the interactions between the user and a recommender system by incorporating different interfaces and response techniques. In specific, we propose an interactive mirror to extract the best two-dimensional shape of the human body skeleton. Then, we can identify who's standing in front of the mirror using a camera. A user interface is developed to map the skeleton points tracked by a Kinect camera using a star skeleton strategy. Then, the user recognition is used into building a personalized recommendation outputs such as multimedia contents (e.g.,  movies and music).