An Integrated Recommender System for Automated Playlist Expansion - Shaktikumar V. Patel (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The rapid growth of digital music platforms has made it challenging for users to curate and maintain playlists. To tackle this concern, an automated playlist extension system has been devised to aid users in creating seamless and personalized playlists. This article proposes a novel recommender system that initially retrieves large set of tracks/songs through collaborative filtering techniques then re-ranks the retrieved songs to enhance the accuracy of the music playlist recommendations. With this integrated approach of recommending songs, users get seamless experience in continuation of playlist. Through the experimental evaluations using a real-world dataset provided by Spotify, the integrated recommender system exhibits superior performance in recommendation accuracy and user satisfaction compared to alternative collaborative filtering methods. The findings witness the advantages of integrating various recommendation methodologies to enhance the robustness and personalization of music playlist expansion.