Music recommendation system based on the continuous combination of contextual information

With the progress of technology in music players, especially in intelligent cell phones, users have access to large archives. Quick and easy selecting favorite music among these large archives becomes one of the biggest problems for users. For example, selecting music in a silent forest is different from a crowded street or feelings for listening to music in a morning of a working day is different from an afternoon of a holiday. In this paper, a system has been designed that it collects users' context information such as weather, temperature, geographical position, etc., and according to a weighted combination of them, it recommends an appropriate music that is a user's favorite at the moment. Thus, this system includes a rating method that determines how close are music's context, which have been played before, in the moment's context and recommend the music that has the most closeness. The result of this research shows that recommendations that this system makes in different conditions, is closed to the user's choice.