We present a music recommendation system that uses different features of audio content for each user based on the user's listening history. The system is based on the idea that different people may give more importance to certain aspects of music. MFCC, MPITCH, BEAT, STFT feature sets are obtained for all the available songs and then different clusterings of the songs based on each possible feature set is obtained. When a user session is observed, the cluster ids of songs the user listened in each clustering are obtained. The clustering that has been able group the users' songs in the best possible way according to Shannon entropy is selected as the right clustering for that user. Using this content based recommendation scheme, as opposed to a static set of features resulted in up to 60 percent increase in recommendation success.