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


MARC21 XML Export

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    <subfield code="a">Léa Briand</subfield>
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    <subfield code="a">Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</subfield>
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    <subfield code="a">&lt;p&gt;We publicly release&amp;nbsp;the anonymized&amp;nbsp;&lt;em&gt;song_embeddings.parquet&amp;nbsp; user_embeddings.parquet&amp;nbsp; user_features_test.parquet&amp;nbsp; user_features_train.parquet&amp;nbsp; user_features_validation.parquet&lt;/em&gt;&amp;nbsp;datasets, with each of the&amp;nbsp;TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the&amp;nbsp;article &amp;quot;&lt;em&gt;A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps&amp;quot;&lt;/em&gt;&amp;nbsp;published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining&amp;nbsp;(&lt;em&gt;KDD 2021&lt;/em&gt;). The paper is available&amp;nbsp;&lt;a href="https://arxiv.org/abs/2106.03819"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;These datasets are used in the&amp;nbsp;GitHub repository&amp;nbsp;&lt;a href="https://github.com/deezer/semi_perso_user_cold_start"&gt;deezer/semi_perso_user_cold_start&lt;/a&gt;&amp;nbsp;to reproduce experiments from the article.&lt;/p&gt;

&lt;p&gt;Please cite our paper if you use our code or data in your work.&lt;/p&gt;</subfield>
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