Adaptive Music Recommendation System Using Collaborative Filtering and Deep Learning

Abstract

This research focuses on the development of an adaptive music recommendation system that combines collaborative filtering and deep learning to improve the accuracy and relevance of music suggestions. The primary problem addressed in this study is the limitation of traditional recommendation methods, such as collaborative filtering, which struggle with issues like data sparsity and the cold start problem. The objective of the research is to design a hybrid recommendation model that enhances collaborative filtering by integrating deep learning techniques to capture complex, nonlinear relationships between users and items. The proposed method employs user-item interactions, such as ratings and listens, to create an initial recommendation model using matrix factorization or nearest neighbor techniques to predict unknown preferences. To further refine the recommendations, deep neural networks (DNNs) are utilized, specifically through multi-layer perceptrons (MLPs) or autoencoders, to analyze intricate patterns and temporal dynamics in user behavior. The findings indicate that the hybrid model leads to a 20% increase in recommendation accuracy compared to traditional methods, demonstrating superior performance in predicting user preferences. Additionally, users reported a more personalized experience with fewer irrelevant recommendations, improving overall user satisfaction. The model was trained using the Adam optimizer and appropriate loss functions to ensure optimal performance. In comparison with traditional collaborative filtering, the hybrid system adapts more effectively to changing user preferences, providing more accurate and diverse music suggestions. In conclusion, the proposed adaptive system significantly enhances recommendation accuracy and user engagement. Future work should explore additional deep learning architectures, such as convolutional and recurrent neural networks, and investigate the use of real-time data to further personalize recommendations. The system has great potential for application across various music platforms, offering users highly personalized and relevant music suggestions.