Collaborative Filtering for Music Recommendations using Deep Neural Networks

The standard method that can be found in music recommendation systems is Collaborative Filtering (CF), which is based on user-item interaction patterns through which user preferences can be predicted. We describe new techniques that leverage Deep Neural Networks (DNNs) to improve further CF approaches by alleviating classical CF challenges, like data sparsity and cold-start issues. Using DNNs, we learn complex and non-linear interactions between users and items, learning latent features while learning representation even at a hidden layer, which CF models didn't initially know. We adapt our architecture to learn high-dimensional embedding for users and music tracks by combining matrix factorization with DNN layers. This approach leverages the benefits of a hybrid system adept at handling implicit feedback that is more commonly available in music streaming services as opposed to explicit user inputs, which may be smaller in number and sparse. We also use methods like dropout and batch normalization to optimize network performance and avoid overfitting. We show that on large-scale data sets of music tracks, our method significantly outperforms classical CF approaches in terms of recommendation accuracy. Our results indicate that DNNs improve the robustness and scalability of CF systems and yield high precision in order-dependent personalized music recommendation. This study emphasizes the strength of DNNs in bringing about the CF-based Recommender revolution through the adoption of deep learning concurrent with dynamic user behaviors and diverse music catalogs. In future work, we will concentrate on real-time recommendation capability and broaden the model applicability for multi-modal music consumption contexts.