Heartbeats: music recommendation system with fuzzy inference engine

Vinothini Kasinathan (Asia Pacific University of Technology and Innovation)
Aida Mustapha (Universiti Tun Hussein Onn Malaysia)
Tan Sau Tong (Asia Pacific University of Technology and Innovation)
Mohamad Firdaus Che Abdul Rani (Asia Pacific University of Technology and Innovation)
Nor Azlina Abd Rahman (Asia Pacific University of Technology and Innovation)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 16, No 1: October 2019

In developing a music recommendation system, there are several factors that can contribute to the inefficiency in music selection. One of the problems persists during the music listening is that common music playing application lacks the ability to acquire context of the user. Another problem that common music recommendation system fails to address the is emotional impact of the recommended song. To address this gap, this paper presents a music recommendation system based on fuzzy inference engine that considers user activities and emotion as part of the recommendation parameters. The system includes building a smart music recommendation system that has user profiling capabilities to recommend correct songs based on the user’s preferences, mood and time. Findings of the this paper have shown that Heartbeats’s fuzzy inference engine has successfully achieved its aim, which is to improve users’ music listening experience by giving suitable song recommendation based on user context situation.