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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  <identifier identifierType="DOI">10.5281/zenodo.5121674</identifier>
  <creators>
    <creator>
      <creatorName>Léa Briand</creatorName>
      <affiliation>Deezer Research</affiliation>
    </creator>
    <creator>
      <creatorName>Guillaume Salha-Galvan</creatorName>
      <affiliation>Deezer Research</affiliation>
    </creator>
    <creator>
      <creatorName>Walid Bendada</creatorName>
      <affiliation>Deezer Research</affiliation>
    </creator>
    <creator>
      <creatorName>Mathieu Morlon</creatorName>
      <affiliation>Deezer Research</affiliation>
    </creator>
    <creator>
      <creatorName>Viet-Anh Tran</creatorName>
      <affiliation>Deezer Research</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Deezer dataset</subject>
    <subject>user embedding</subject>
    <subject>song embedding</subject>
    <subject>Recommender Systems</subject>
    <subject>Music Streaming App</subject>
    <subject>Cold start</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-07-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5121674</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5121673</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
</resource>
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