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2 results for “Deezer Dataset”
Deezer Podcast Dataset for Topic Modeling
<p>We release a new dataset consisting of podcast metadata (title and description) for 29 539 shows. This dataset can be used to reproduce the experiments from the article <em>Topic Modeling on Podcast Short-Text Metadata</em> accepted at the <a href="https://ecir2022.org">ECIR 2022</a> conference.</p> <p>More information about this data and how it should be used in experiments can be found in our paper and <a href="https://github.com/deezer/podcast-topic-modeling">GitHub repository</a>.</p> <p>Please cite our paper if you use the code or data.</p>
Deezer listening events dataset
<p><strong>What does this dataset contain?</strong></p> <p>This dataset contains over 700 million time-stamped listening events collected from 3.4M anonymised users on the music streaming service Deezer, occurred between March and August 2022. It includes 50k anonymised songs, among the most popular ones on the service as well as their pre-trained embedding vectors, calculated by our internal model. All files are in parquet format which could be read by using <code>pandas.read_parquet</code> function.</p> <p> </p> <p><strong>What could this dataset be used for?</strong></p> <p>This dataset could be used for collaborative filtering as well as sequential recommendation (including both next-item and next-session recommendations).</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite following paper:</p> <pre><span>@inproceedings</span>{<span>tran-recsys2024</span>, <span>title</span>=<span><span>{</span>Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation<span>}</span></span>, <span>author</span>=<span><span>{</span>Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra and Romain Hennequin<span>}</span></span>, <span>booktitle</span> = <span><span>{</span>Proceedings of the 18th ACM Conference on Recommender Systems<span>}</span></span>, <span>year</span> = <span><span>{</span>2024<span>}</span></span> }</pre>
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