Addressing long tail problem in music recommendation systems Online publication date: Mon, 07-Mar-2022
by M. Sunitha; T. Adilakshmi
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 6, No. 5, 2021
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.
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