Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"

<p>We publicly release&nbsp;the anonymized&nbsp;<em>song_embeddings.parquet&nbsp; user_embeddings.parquet&nbsp; user_features_test.parquet&nbsp; user_features_train.parquet&nbsp; user_features_validation.parquet</em>&nbsp;datasets, with each of the&nbsp;TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the&nbsp;article &quot;<em>A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps&quot;</em>&nbsp;published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining&nbsp;(<em>KDD 2021</em>). The paper is available&nbsp;<a href="https://arxiv.org/abs/2106.03819">here</a>.</p> <p>These datasets are used in the&nbsp;GitHub repository&nbsp;<a href="https://github.com/deezer/semi_perso_user_cold_start">deezer/semi_perso_user_cold_start</a>&nbsp;to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>