[01] Syam Murali, Enterprise Business Analytics, Institute of Systems Science, National University of Singapore, Singapore, Singapore. [02] Upma Vermani, Enterprise Business Analytics, Institute of Systems Science, National University of Singapore, Singapore, Singapore. [03] Catherine Khaw, Enterprise Business Analytics, Institute of Systems Science, National University of Singapore, Singapore, Singapore.

Recommendation systems used to recommend music are different from those used to recommend products/services. A user is not always interested in a familiar and regular song recommendation which he is already aware of. Instead, users who like to explore new music are looking for diverse and new songs which they would not have heard otherwise. The engine should also ensure that the songs are not too far off from the user’s taste that the user does not like it. Therefore, the key is a balanced recommendation. This paper addresses this problem by recommending novel and unfamiliar songs to the users based on their desire to explore music along with familiar recommendations. The recommendation engine was tested on certain parameters through a user survey and it was found to increase user satisfaction, particularly for the users who like exploring music.

Recommendation Systems, Unfamiliar Songs, Collaborative Filtering, Recommender Systems, Music Exploration, Novel Songs

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