Related Rhythms: Recommendation System To Discover Music You May Like

• URL: http://arxiv.org/abs/2309.13544v1
• Date: Sun, 24 Sep 2023 04:18:40 GMT
• Title: Related Rhythms: Recommendation System To Discover Music You May Like
• Authors: Rahul Singh and Pranav Kanuparthi
• Abstract summary: In this paper, a distributed Machine Learning 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. The objective of the proposed application is to leverage an ML system trained to optimally recommend songs that a user might like.
• Score: 2.7152798636894193
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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