A personalized music recommender service based on Fuzzy Inference System

Abstract

In this paper, we are proposing a personalized music recommender service based on Mamdani Fuzzy Interference System (M-FIS). Collection of playlist is used for gathering users' choice and mood while listening to songs. Similarity between audio files is calculated based on Mel Frequency Cepstral Coefficients (MFCC). We have developed a recommender model based on M-FIS with the aforementioned similarities and playlists. We were able to gain an acceptable accuracy rate using FIS compared to other method reported in literature.