Title: Addressing long tail problem in music recommendation systems

Authors: M. Sunitha; T. Adilakshmi

Addresses: Vasavi College of Engineering, Hyderabad, 500089, India ' Vasavi College of Engineering, Hyderabad, 500089, India

Abstract: Music recommendation systems (MRS) are the information filtering tools used to handle information overloading problem in the music field. Collaborative filtering (CF) is the most frequently used approach to provide recommendations. Even though CF is very simple and popular but it faces the problem of popularity bias. Research to discover the songs which are not popular but might be interesting to a user is an interesting direction in music recommendation systems. This paper proposes a multi-stage graph-based method and a KNN-based method to identify and recommend less popular songs which are also known as long tail songs. MSG_WEIGHTS finds the recommendation vector based on the weights. Two variants MSG_KNN, MSG_K-means are proposed to identify tail songs. Second method applies KNN to identify relatively less frequent songs for recommendation. Results obtained show that proposed methods are able to identify novel songs from the tail for recommendation.

Keywords: music recommendation systems; MRS; information overloading; multi-stage graph; head; mid; tail.

DOI: 10.1504/IJCSYSE.2021.121367

International Journal of Computational Systems Engineering, 2021 Vol.6 No.5, pp.246 - 254

Received: 17 Apr 2021
Accepted: 12 Aug 2021

Published online: 03 Mar 2022 *

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