Music Recommendation
30 papers with code • 1 benchmarks • 3 datasets
Libraries
Use these libraries to find Music Recommendation models and implementations
YouTube8M-MusicTextClips
Music4All-Onion
MuseChat Dataset
Most implemented papers
Knowledge Graph Convolutional Networks for Recommender Systems
hwwang55/KGCN
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To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items.
Deep Content-User Embedding Model for Music Recommendation
jongpillee/deep-content-user
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Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
Item-based Variational Auto-encoder for Fair Music Recommendation
parkjinhyeock/evalrs-submission
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Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF).
Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework
Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors.
MusicMood: Predicting the mood of music from song lyrics using machine learning
Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively.
A Deep Multimodal Approach for Cold-start Music Recommendation
Second, track embeddings are learned from the audio signal and available feedback data.
Recognizing Musical Entities in User-generated Content
Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity.
Deep Learning-Based Automatic Downbeat Tracking: A Brief Review
Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area.
Exploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i. e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences.
