Year: 2024
Type: article
Abstract: This paper presents a novel approach to music recommendation that leverages intrinsic musical properties to address the limitations of traditional collaborative filtering methods, particularly the cold-start problem.Unlike existing systems that rely heavily on user behavior and historical data, our proposed method utilizes a neural network trained on fundamental attributes of music-such as tempo, key, mode, duration, and loudness-to generate recommendations.We detail the process of extracting these musical properties using tools like the librosa library and a custom function, followed by training a neural network to predict user preferences based solely on these features.The system's performance was evaluated using the Million Song Dataset, achieving an accuracy of 76.75%.Despite its potential, the method faces challenges related to computational efficiency and the handling of diverse musical genres.Future work includes exploring hybrid models combining collaborative filtering with feature-based recommendations and testing alternative algorithms to enhance performance and applicability.
Source: Journal of Student Research
Author: Arush Srivastava
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413564325