In 2024, the music streaming service Deezer had ****million subscribers worldwide. This represents a ***** percent decrease from the previous year, when the figures were approximately **** million. Deezer: further informationDeezer is a French company headquartered in Paris and founded in 2007. The online music service is popular in its country of origin – a Statista survey revealed that more digital music content buyers in France had purchased music downloads from Deezer than any other provider. Deezer also ranked as one of the most used mobile apps among ** to 34-year-olds in France, outperforming Google and Twitter. Despite success in France and the Netherlands, Deezer is far less popular in the United Kingdom and Germany and has seriously struggled across the Transatlantic too. In the United States, the music streaming industry is dominated by Spotify, Pandora and Apple Music, all of which are constantly engaged in the battle for the top spot as U.S. market leader. As Deezer's worldwide share of music streaming subscribers is *** times smaller than Spotify’s, the company would need to break America in a big way to have a significant impact on the market. On a more positive note though, Deezer Music ranks as one of the leading music apps in the Apple App Store worldwide in terms of monetization in 2022. This is proof that smaller companies can still be successful in the industry if they have something different to offer. CEO Hans-Holger Albrecht has previously stated that his goal is not to rival the likes of Spotify and that he is keen to avoid too many comparisons between Deezer and larger competitors. Instead, Albrecht intends for Deezer to appeal to users on a humbler level and for the service to focus on more local markets and needs. In a world driven by the need for internationalization, Deezer’s priorities may seem out of place but will likely satisfy music lovers seeking something the larger services simply cannot or are not willing to provide.

In 2024, the music streaming service Deezer had *** million subscribers worldwide. Out of this number, *** million were attributed to direct subscribers in France. Partnership subscribers accounted for *** million that year.

This statistic shows the total number of users of Spotify, Apple Music and Deezer in the Netherlands as of the first quarter of 2018 (in thousands). Exact user numbers for certain apps are rare to find in the Netherlands. According to the source, the total number of Spotify users in the Netherlands was forecast to reach *** million in the first quarter of 2018. Additionally, the source mentions that *** million users are paid users. This is because a part of these subscriptions come from a third party, such as telecom provider KPN where Spotify is a part of their telecom package. As of January 2019, Spotify had an online reach, a percentage of visitors that visited content at least once during a certain period of time, of *** million in the Netherlands in January 2019.

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, ** percent of respondents in Egypt were aware of Deezer as a music streaming platform. In comparison, *** percent of respondents were loyal to the brand. Anghami was the music streaming leader in Egypt in that year.

What does this dataset contain?

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 pandas.read_parquet function.

What could this dataset be used for?

This dataset could be used for collaborative filtering as well as sequential recommendation (including both next-item and next-session recommendations).

Citation

If you use this dataset, please cite following paper:

@inproceedings{tran-recsys2024,
 title={Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation},
 author={Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra and Romain Hennequin},
 booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},
 year = {2024}
}

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: "work, gym, party, sleep | morning, run, night, dance | car, train, relax, club".

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: "linear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type". The users are represented as embeddings 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 : "age, country, gender".

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: https://github.com/KarimMibrahim/Situational_Session_Generator.git

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: https://developers.deezer.com/api. Each user is represented with an anonymized user_id 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].

Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: ColdTrack (no overlap of tracks between the splits), ColdUser (no overlap of users between the splits), and WarmCase (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "fold" number in each condition.

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, ** percent of respondents who were monogamous users of the music streaming platform Deezer were from the United Arab Emirates (UAE). Anghami had the largest share of monogamous users in the MENA region.

The data was collected from the music streaming service Deezer (November 2017). These datasets represent friendship networks of users from 3 European countries. Nodes represent the users and edges are the mutual friendships. We reindexed the nodes in order to achieve a certain level of anonimity. The csv files contain the edges -- nodes are indexed from 0. The json files contain the genre preferences of users -- each key is a user id, the genres loved are given as lists. Genre notations are consistent across users. In each dataset users could like 84 distinct genres. Liked genre lists were compiled based on the liked song lists. The countries included are Romania, Croatia and Hungary. For each dataset we listed the number of nodes an edges.

Dataset Card for Deezer ego nets

  Dataset Summary

The Deezer ego nets dataset contains ego-nets of Eastern European users collected from the music streaming service Deezer in February 2020. Nodes are users and edges are mutual follower relationships.

  Supported Tasks and Leaderboards

The related task is the binary classification to predict gender for the ego node in the graph.

  External Use





  PyGeometric

To load in PyGeometric, do the… See the full description on the dataset page: https://huggingface.co/datasets/graphs-datasets/deezer_ego_nets.

This statistic indicates the frequency of penetration of Deezer among internet users in Spain in 2013, by listening behavior. A total of 3.9 percent of respondents are users of Deezer, although this share was higher among those who considered themselves passionate about music.

This is a user-aware music dataset labeled with the contextual use of each track according to each user. The dataset is composed of 10 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, gym, happy, night, relax, running, sad, summer, work, workout". For each track/user pair, a contextual tag is associated with it indicating that the user listens to the track in the associated context. Additionally, the users are represented as embeddings based on their listening history computed through the matrix factorization of the user/track matrix. The creation of the dataset and the baseline of our auto-tagging model is described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Should we consider the users in contextual music auto-tagging models?" 21st International Society for Music Information Retrieval Conference (ISMIR). 2020. The source code of the paper is available here: https://github.com/KarimMibrahim/user-aware-music-autotagging The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track/user pair is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an anonymized USER_ID which is associated with the user embedding available in the user_embeddings.csv file.

How high is the brand awareness of Deezer in the UK?When it comes to digital music users, brand awareness of Deezer is at **% in the UK. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Deezer in the UK?In total, **% of UK digital music users say they like Deezer. However, in actuality, among the **% of UK respondents who know Deezer, **% of people like the brand.What is the usage share of Deezer in the UK?All in all, **% of digital music users in the UK use Deezer. That means, of the **% who know the brand, **% use them.How loyal are the users of Deezer?Around *% of digital music users in the UK say they are likely to use Deezer again. Set in relation to the **% usage share of the brand, this means that **% of their users show loyalty to the brand.What's the buzz around Deezer in the UK?In July 2022, about **% of UK digital music users had heard about Deezer in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's little buzz around Deezer in the UK.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

We have publicly released the anonymized "new_release_stream.csv" dataset from the music streaming platform Deezer. This dataset is described in detail in the article titled "Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect", which was published in the proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023).

Each row in the dataset contains an anonymized user and item identifier, a reference timestamp in seconds (measured from the first consumption in the dataset), and a binary value "y". This "y" value indicates whether a song was listened to for more than 80% of its duration (y = 1) or not (y = 0).

You can find this dataset in the GitHub repository deezer/ex2vec, where it is used to reproduce experiments discussed in the article.

If you plan to use our code or data in your work, please make sure to cite our paper accordingly.

@inproceedings{sguerra2023ex2vec,
 title={Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect},
 author={Sguerra, Bruno and Tran, Viet-Anh and Hennequin, Romain},
 booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},
 year = {2023}
}

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, **** percent of respondents in the region preferred Deezer as a music streaming platform. Anghami was the music streaming leader in the region in that year.

The size of the Cloud Music Services Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 13.00% during the forecast period. The cloud music services industry encompasses the delivery of music through cloud-based platforms, enabling users to stream, download, and store music online. This industry has seen exponential growth driven by technological advancements, changing consumer preferences, and the rise of mobile devices. Major players in the market include Spotify, Apple Music, Amazon Music, and YouTube Music, each offering unique features such as personalized playlists, offline listening, and music discovery algorithms. The transition from physical music media to digital formats has further accelerated the adoption of cloud music services, as consumers increasingly prefer the convenience of accessing vast music libraries anytime, anywhere. The industry's growth is significantly influenced by the rise of subscription-based models, which provide users with ad-free experiences and additional features for a monthly fee. This shift has prompted a decline in traditional music purchasing methods, such as CDs and digital downloads, as streaming becomes the preferred mode of consumption. Moreover, social media and user-generated content have enhanced the visibility of cloud music services, facilitating viral trends and artist promotion. Recent developments include: January 2023: With 14% YoY growth, Spotify announced its premium subscriber base touched 205 million, making it the world's first music streaming player with such a large user base. At the beginning of 2022, Spotify announced an investment of $100 million in the licensing, development, and marketing of music and audio content from historically underrepresented creators. These factors helped Spotify reach a milestone in the music industry., November 2022: Amazon Prime increased the limits for its users by allowing access to 100 million songs, up from 2 million earlier. The catalog will now match the rival Apple Music Stream. Under the Prime membership, 'All-Access' playlists will be available for on-demand listening with an advertising-free option. The new catalog will carry Amazon-exclusive podcasts., November 2022: Deezer, another player in music streaming, partnered with Dazn, which streams live sports in Italy. With this partnership, Dazn subscribers will enjoy their favorite tracks, albums, and playlists on Deezer, along with football and other premium sports content.. Key drivers for this market are: Higher Demand for In-App Purchase Driving the Market, Market Growth Aided by Robust Smart Phone Penetration and Internet Coverage. Potential restraints include: Concerns Relating to Understanding the Changing Behaviour Pattern of the Consumers. Notable trends are: Increasing Penetration of Smartphones and Tablets drives the Market for Cloud Music Services.

The global cloud music services market is experiencing robust growth, projected to reach a significant value by 2033. A compound annual growth rate (CAGR) of 13% from 2025 to 2033 indicates substantial market expansion driven by several key factors. The increasing penetration of smartphones and affordable internet access globally fuels widespread adoption of streaming services. Consumer preference for on-demand music access and the convenience of vast digital libraries further contribute to market expansion. Furthermore, the development of innovative features like personalized playlists, high-fidelity audio, and social interaction within music platforms enhances user experience and drives engagement. The industry is witnessing a trend towards personalized music experiences, leveraging advanced algorithms and user data to curate content and improve recommendations. Competitive pressures from established players like Spotify, Apple Music, and Amazon Music, alongside emerging regional players, contribute to ongoing innovation and service improvements. However, factors such as fluctuating data costs in certain regions, concerns around music licensing and royalty payments, and competition from free, ad-supported platforms present challenges to sustained growth. Market segmentation reveals varying levels of adoption across regions, with North America and Europe currently holding significant market share, while the Asia-Pacific region shows immense potential for future expansion, given its rapidly growing population and increasing internet usage. The market segmentation offers further insight. A detailed analysis of production, consumption, import, and export patterns across regions reveals significant differences in market dynamics. Price trends are influenced by factors such as licensing costs, competition levels, and value-added features offered by service providers. Key players are actively investing in technological advancements, strategic partnerships, and expansion into new markets to maintain their competitive edge. Future market growth will likely hinge on continued innovation, strategic alliances, and addressing the challenges related to content licensing, pricing strategies, and regional market penetration. The forecast period (2025-2033) presents numerous opportunities for market players to capitalize on expanding consumer bases and evolving consumer preferences. Addressing the evolving regulatory landscape and maintaining a balance between affordability and profitability will be crucial for continued market success. Recent developments include: January 2023: With 14% YoY growth, Spotify announced its premium subscriber base touched 205 million, making it the world's first music streaming player with such a large user base. At the beginning of 2022, Spotify announced an investment of $100 million in the licensing, development, and marketing of music and audio content from historically underrepresented creators. These factors helped Spotify reach a milestone in the music industry., November 2022: Amazon Prime increased the limits for its users by allowing access to 100 million songs, up from 2 million earlier. The catalog will now match the rival Apple Music Stream. Under the Prime membership, 'All-Access' playlists will be available for on-demand listening with an advertising-free option. The new catalog will carry Amazon-exclusive podcasts., November 2022: Deezer, another player in music streaming, partnered with Dazn, which streams live sports in Italy. With this partnership, Dazn subscribers will enjoy their favorite tracks, albums, and playlists on Deezer, along with football and other premium sports content.. Notable trends are: Increasing Penetration of Smartphones and Tablets drives the Market for Cloud Music Services.

Overview

This dataset supports the research paper "Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data," authored by Deezer and CNRS researchers Kristina Matrosova, Lilian Marey, Guillaume Salha-Galvan, Thomas Louail, Olivier Bodini, and Manuel Moussallam as part of the RECORDS initiative (https://records.huma-num.fr/). The paper, accepted at the 18th ACM Recommender Systems Conference (RecSys 2024), explores how recommender algorithms influence the promotion of local music.

Data Description

.inter Files

The .inter files contain the listening histories of 10,000 Deezer users from Brazil (BR), France (FR), and Germany (DE) over a period of 1 months (March 2019). Each record includes user, item (track), and artist IDs. The DEEZER_GLOBAL.inter file is a combined dataset of these three countries.

All IDs have been hashed and reindexed.

Column names: user_id, item_id, artist_id (only for global file)

.csv Files

- user_country.csv: Links each user ID in the global .inter dataset to their country (BR, FR, or DE).
Column names: user_id, country

- metadata_DEEZER Files: Match artist IDs with their countries using three different methods:
- active: Artist’s country of activity
- origin: Artist’s country of origin
- musicbrainz: Country according to the MusicBrainz database (https://musicbrainz.org)

Column names: item_id, country

Nodes represent the pages and edges are mutual likes among them. - The data was collected from the music streaming service Deezer (November 2017). These datasets represent friendship networks of users from 3 European countries. Nodes represent the users and edges are the mutual friendships. We reindexed the nodes in order to achieve a certain level of anonimity. The edge files contain the edges - nodes are indexed from 0. The json files contain the genre preferences of users - each key is a user id, the genres loved are given as lists. Genre notations are consistent across users. In each dataset users could like 84 distinct genres. Liked genre lists were compiled based on the liked song lists. The countries included are Romania, Croatia and Hungary.

In 2020, music streaming reached its highest ever revenue in the Netherlands, growing by roughly ** percent when compared to 2019. The use of services like Spotify and YouTube Music made more revenue than physical sales (including both cd’s as well as vinyl) and digital downloads. The source mentions that music streaming had a market share of ** percent in total revenue of the music industry in the Netherlands in 2020.

What are the most popular music streaming services of the Netherlands?

Exact user numbers for music streaming services are difficult to find in the Netherlands, as companies usually do not share this information in such detail. In the first quarter of 2018, though, Spotify was forecast to reach *** million users (of which *** million paid users), whereas Apple Music and Deezer had around *** million and ******* users respectively. The Netherlands is considered a typical “Spotify-country” by industry experts, as can be seen, for example, from the high install base of the app in 2017 and 2018 as well as the increasing reach between 2014 and 2018.

The rise of podcasts

The numbers shown in this ranking only look at music streaming but do not take podcasts into account. This topic is relatively new in the Netherlands and not yet researched in-depth as compared to other countries. The type of smartphone used, however, seems to be an important driver for which podcast platforms Dutch consumers use the most. Apple Podcasts, for example, comes preinstalled on iOS devices, whereas Spotify and dedicated podcast apps like Overcast were mostly used by Android users (as Google’s operating system does not have native podcast support).

The dataset is composed of 15 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, chill, club, dance, gym, happy, night, party, relax, running, sad, sleep, summer, work, workout". For each track one or multiple tags are associated with it indicating that users listen to the track in the associated context.

The creation of the dataset and the initial baseline of an auto-tagging model is described in the paper: Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC." 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.

The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api

.