Abstract

Individual music preferences can generally depend on severalfactors, namely demographic data such as gender, age, etc., and morespecific psychological factors like mood and personality. However, whilemainstream music applications tend to consider the former, they usuallyignore psychological factors that would otherwise allow for more accu-rate recommendations. In this paper, we attempt to develop a smartmusic recommendation system by comparing various machine-learningalgorithms to recommend a genre based on the user’s current mood,which is determined using a psychological scale, along with their basicdemographic data. A custom dataset was built, where the TIPI scalewas used to identify users’ personality types, which we then fed into themodels to generate recommendations. Based on the results, ensemble al-gorithms performed the best for the moods of “Happy” and “Gloomy”,while semi-supervised algorithms achieved the best scores for the moodsof “Stressed” and “Relaxed”