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6 results for “Music streams”
Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset
<p>This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>.</p> <p>Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>".</p> <p>The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: <a href="https://github.com/KarimMibrahim/Situational_Session_Generator.git">https://github.com/KarimMibrahim/Situational_Session_Generator.git</a></p> <p>The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a>. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. </p> <p>Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition. </p>
Dataset: Music Industry Professionals' Perspectives on Music Streaming Services and Recommendation
<p><strong>Questionnaire response data set</strong><br> Here, we include the data retrieved from participants at Eurosonic Noorderslag 2023, as described in the paper cited above.<br> When using, analyzing, or publishing this data in any way, please make sure to attribute it to the authors and cite it accordingly.<br> <br> We include the data in .xlsx, .csv format (semicolon-separated, and .tsv format (tab-separated). We suggest using the Excel file, as its layout makes it more easily readable.<br> <br> The complete question list as used in the questionnaire is published separately on <a href="https://doi.org/10.5281/zenodo.8121151">https://doi.org/10.5281/zenodo.8121151</a>.<br> <br> <strong>Paper title</strong><br> Looking at the FAccTs: Exploring Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendations<br> <br> <strong>Paper abstract</strong><br> Music recommender systems, commonly integrated into streaming services, help listeners find music. Previous research on such systems has focused on providing the best possible recommendations for these services' consumers, as well as on fairness for artists who release their music on streaming services. While those insights are imperative, another group of stakeholders has been omitted so far: the many other professionals working in the music industry. They, too, are (in)directly affected by music streaming services. Therefore, this work explores the perspective of music industry professionals. We present a study that addresses the role of streaming services and recommender systems in their jobs. Results indicate this role is significant. Furthermore, participants feel that music recommender systems lack transparency and are insufficiently controllable, for both customers and artists. Finally, participants desire that music streaming services take charge of increasing recommendation diversity, and variety in consumers' listening behavior and taste.</p> <p><strong>Citation</strong><br> Karlijn Dinnissen, Isabella Saccardi, Marloes Vredenborg, and Christine Bauer. 2023. Looking at the FAccTs: Exploring Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendations. In 2nd International Conference of the ACM Greek SIGCHI Chapter (CHIGREECE 2023), September 27–28, 2023, Athens, Greece. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3609987.3610011</p>
Datasets from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits"
<p>We publicly release the anonymized <em>user_features.csv</em> and <em>playlist_features.csv</em> datasets, from the music streaming platform Deezer, as described in the article "<em>Carousel Personalization in Music Streaming Apps with Contextual Bandits"</em> published in the proceedings of the 14th ACM Conference on Recommender Systems (<em>RecSys 2020</em>). The paper is available <a href="https://arxiv.org/abs/2009.06546">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/carousel_bandits">deezer/carousel_bandits</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"
<p>We publicly release the anonymized <em>song_embeddings.parquet user_embeddings.parquet user_features_test.parquet user_features_train.parquet user_features_validation.parquet</em> datasets, with each of the TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the article "<em>A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</em> published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining (<em>KDD 2021</em>). The paper is available <a href="https://arxiv.org/abs/2106.03819">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/semi_perso_user_cold_start">deezer/semi_perso_user_cold_start</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
Dataset for the paper "The skipping behavior of users of music streaming services and its relation to musical structure"
<p>Dataset for the paper "The skipping behavior of users of music streaming services and its relation to musical structure" by Nicola Montecchio, Pierre Roy, François Pachet - 10.1371/journal.pone.0239418</p>
Survey data on the appreciation of live music and on live streamed music in China
<p>This survey is part of an ethnographic research for Francesca Cireddu's PhD thesis, titled "Partecipazione ed esperienza live nei concerti pop-rock in tempi di transizione: riflessioni e proposte metodologiche a partire da alcuni casi tratti dai contesti alternativi di USA, Gran Bretagna, Italia e Cina" (trad. "Participation and live experience in pop-rock gigs in times of transitions: reflections and methodological suggestions on the basis of some case studies on alternative music contexts in USA, Great Britain, Italy and China").</p> <p>Data show the value attached to live music in Italy with regards to it being a "place" for aggregation and socialisation. <br> Only three people replied, thus these data are not reliable, even though they offer a starting point for some preliminary considerations. </p> <p>The answers are useful in order to highlight possible incidence between sociocultural practices and live music practices in specific local areas in China with regards to socialising habits. These data also show how such aspects are relevant in evaluating the way live streaming music performances may or may not be positively evaluated in China, especially with regards to the widespread use of participatory interfaces such as "bullet comments" (<em>danmu </em>弹幕).</p>
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