A Contemplation on Music Recommendation Systems Based on Emotion Detection

Abstract

Rise of music streaming platforms have attracted large number of users. This increase in the userbase has given birth to competitive market and competition to pull more number of users by providing quality service. Quality of service on these streaming platforms can be achieved by sensing the user needs and customizing the dashboards or playlist as per their need. This responsibility of customized recommendation lies on the recommendor system, an integral part of streaming platforms. In the absence of an effective recommender system, users have to waste lot of time in finding what they want, sometimes this is very frustrating and may lead to loss in revenue. It is found that “Emotion” play an important role in user music preferences, yet there is very little work done in this sector. In this paper, we have discussed taxonomy of a recommender system, critically analyzed the prominent existing models and have proposed a new hybrid model. The proposed model is an amalgamation of emotion detected from face, lyrical recommendation system and users history.