Related Rhythms: Recommendation System To Discover Music You May Like
This work addresses the need for better user experience and engagement in music streaming services, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of music recommendation by developing a distributed machine learning pipeline that takes a subset of songs as input and outputs similar songs, using the Million Songs Dataset to enable efficient audio track analysis and recommendations without commercial platforms.
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.