Recommendation System based on Artist and Music Embeddings


  • Akshat Surolia Associate Data Scientist, D S Matics, Pune, Maharashtra.


Personalized Music, Artist, Spotify’s API, Embeddings


In this paper, we present a personalized music recommendation system based on the embeddings of artists and music. The main peculiarity of our work is that the determining factors of a user’s preferences for a developed music recommender system are the artists and the music that they listen to. The artist embeddings inform the network about the contextual representation of artists in a latent space, where similar artists are closer to each other. The music embeddings hold the information about the music. Both embeddings are then combined to form a new embedding, which is then used to predict the user’s preferences. We use the Spotify’s API to collect the data to train and evaluate the model. Two approaches of building a music recommender system are considered in this paper. Each approach significantly differs in the way the embeddings are learned.


Alice Wang, Aasish Pappu, H. C. (2021). Representation of Music Creators on Wikipedia, Differences in Gender and Genre | Proceedings of the International AAAI Conference on Web and Social Media.

Dai, H., Wang, Y., Trivedi, R., & Song, L. (2016). Deep Coevolutionary Network: Embedding User and Item Features for Recommendation.

Hansen, C., Hansen, C., Maystre, L., Mehrotra, R., Brost, B., Tomasi, F., & Lalmas, M. (2020). Contextual and Sequential User Embeddings for Large-Scale Music Recommendation. RecSys 2020 - 14th ACM Conference on Recommender Systems, 53–62.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013, January 16). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings.

Saravanou, A., Tomasi, F., Mehrotra, R., & Lalmas, M. (2021). Multi-Task Learning of Graph-Based Inductive Representations of Music Content. Proceedings of the 22th International Society for Music Information Retrieval Conference, ISMIR 2021.

Schedl, M. (2019). Deep Learning in Music Recommendation Systems. In Frontiers in Applied Mathematics and Statistics (Vol. 5, p. 44). Frontiers Media S.A.

Shakirova, E. (2017). Collaborative filtering for music recommender system. Proceedings of the 2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference, ElConRus 2017, 548–550.

Stoller, D., Ewert, S., & Dixon, S. (2019). Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators.

Sturm, B. L. (2013). Classification accuracy is not enough: On the evaluation of music genre recognition systems. Journal of Intelligent Information Systems, 41(3), 371–406.




How to Cite

Surolia, A. (2022). Recommendation System based on Artist and Music Embeddings. GLS KALP – Journal of Multidisciplinary Studies, 2(3), 8–15. Retrieved from