Related Rhythms: Recommendation System To Discover Music You May Like

TL;DR

This paper presents a distributed machine learning pipeline for music recommendation, utilizing the Million Songs Dataset to identify songs similar to user-selected inputs, aiming to enhance user engagement and experience.

Contribution

It introduces a scalable ML system that efficiently recommends similar songs using large-scale audio data without relying on commercial platforms.

Findings

Effective identification of similar songs from large datasets

Utilization of distributed ML for scalable recommendations

Potential to improve user engagement in music platforms

Abstract

Machine Learning models are being utilized extensively to drive recommender systems, which is a widely explored topic today. This is especially true of the music industry, where we are witnessing a surge in growth. Besides a large chunk of active users, these systems are fueled by massive amounts of data. These large-scale systems yield applications that aim to provide a better user experience and to keep customers actively engaged. In this paper, a distributed Machine Learning (ML) pipeline is delineated, which is capable of taking a subset of songs as input and producing a new subset of songs identified as being similar to the inputted subset. The publicly accessible Million Songs Dataset (MSD) enables researchers to develop and explore reasonably efficient systems for audio track analysis and recommendations, without having to access a commercialized music platform. The objective of the proposed application is to leverage an ML system trained to optimally recommend songs that a user might like.