Personalized Music Recommendation via User Demographics
In today’s Internet era, Recommender systems have come into existence to solve information overload problem by guiding the general public from the vast pool of options.With an increase in number of free music resources on various platforms the capacity of listening to several number of songs is exceeding for an average individual. Choosing the best songs among the millions of songs is often a daunting task. Further, the service providers to increase their sales and customer satisfaction, need an efficient Recommender system that can generate quality and personalized recommendations.
AMusic Recommenderis proposed combining collaborative filtering with demographic datato improve recommendation precision. The main objective of our proposed Music Recommender is to aggregate the result of user similarity and demographic similarity. Finally, we compare the existing techniques and proposed hybrid technique and determine whether demographic data could improve music recommendations.