Music recommendation system approaches in machine learning

• Main Authors: Pasha, Syed Nawaz, Ramesh, Dadi, Mohmmad, Sallauddin, Navya, P., Kishan, P. Anil, Sandeep, C. H.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Collaboration; Filtration; Machine learning; Music; Recommender systems; Websites
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0081795

Recommendation systems play a vital role in our day to day lives whether it may provide recommendation on which movie to watch or which item to buy. Most of the popular e-commerce websites are now a day's using recommender system gathering customer preferences like Amazon and Flipkart for selling their products. Similar to that we have music streaming applications like wynk and gaana.com which also provide recommendations to its users. These recommendations systems will benefit both the business and the users, the business are getting benefits and users are getting what they like.In this paper we have investigated music recommendation systems like content based filtering and collaborative filtering which provides music recommendations[1] .We also tried to draw correlation between users and songs to provide recommendations as to which user would prefer to listen to which music based on their listening history. For this investigation we used the million song dataset available from kaggle.We used both the content based filtering and collaborate filtering algorithms to provide best music recommendation. Finally we also compared the accuracy levels of both the recommendation systems[2].