Dataset Open Access

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

Léa Briand; Guillaume Salha-Galvan; Walid Bendada; Mathieu Morlon; Viet-Anh Tran


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    "doi": "10.5281/zenodo.5121674", 
    "description": "<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>\n\n<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>\n\n<p>Please cite our paper if you use our code or data in your work.</p>", 
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    "title": "Datasets from the KDD 2021 article \"A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps\"", 
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