Music Recommendation System Using Deep Learning

Abstract

Abstract: As part of this study, we present a music recommendation system that makes use of deep learning. The system learns a neural network that can anticipate the user's musical preferences and provide personalised playlist suggestions based on the user's listening habits. The proposed method considers both the user's explicit tastes and those that may be deduced from their listening patterns. The technology is able to adapt to a user's evolving preferences over time and provide more relevant recommendations. Software that predicts what a user will wish to buy based on their tastes and prior purchases is called a recommendation system. Although the emphasis of this paper is on improving music recommendation systems, the approach outlined here might be applied to a broad range of other platforms and domains as well, including video sharing sites like YouTube and Netflix as well as online retailers like Amazon. System efficiency decreases as complexity increases. Our technique, Tunes Recommendation System (T-RECSYS), provides an efficient recommendation system that can make predictions in real time by integrating data from both content-based and collaborative filtering into a deep learning classification model. By applying our strategy to the Spotify Recsys Challenge dataset, we find a threshold that provides an optimal trade-off between false positives and false negatives, increasing accuracy to 88%