Spotify Million Playlist Dataset Challenge

Summary

The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).

Background

Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.

Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.

But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.

The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?

By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.

Dataset

To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.

In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.

Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.

Dataset Contains

1000 examples of each scenario:

Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks

Download Link

Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files

How many paid subscribers does Spotify have? As of the first quarter of 2025, Spotify had 268 million premium subscribers worldwide, up from 239 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market.
Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

• Genre Networks: Success-based genre collaboration networks
• Genre Mapping: Genre mapping from Spotify genres to super-genres
• Artist Networks: Success-based artist collaboration networks
• Artists: Some artist data
• Hit Songs: Hit Song data and features
• Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20,
 title = {Detecting Collaboration Profiles in Success-based Music Genre Networks},
 author = {Gabriel P. Oliveira and 
      Mariana O. Silva and 
      Danilo B. Seufitelli and 
      Anisio Lacerda and
      Mirella M. Moro},
 booktitle = {21st International Society for Music Information Retrieval Conference}
 pages = {726--732},
 year = {2020}
}

Analysis of ‘Top Spotify Tracks of 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nadintamer/top-tracks-of-2017 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Top Spotify Tracks of 2017

At the end of each year, Spotify compiles a playlist of the songs streamed most often over the course of that year. This year's playlist (Top Tracks of 2017) included 100 songs. The question is: What do these top songs have in common? Why do people like them?

Original Data Source: The audio features for each song were extracted using the Spotify Web API and the spotipy Python library. Credit goes to Spotify for calculating the audio feature values.

Data Description: There is one .csv file in the dataset. (featuresdf.csv) This file includes:

• Spotify URI for the song
• Name of the song
• Artist(s) of the song
• Audio features for the song (such as danceability, tempo, key etc.)

A more detailed explanation of the audio features can be found in the Metadata tab.

Exploring the Data: Some suggestions for what to do with the data:

• Look for patterns in the audio features of the songs. Why do people stream these songs the most?

• Try to predict one audio feature based on the others

• See which features correlate the most

--- Original source retains full ownership of the source dataset ---

This dataset features over 51,000 user reviews for the Spotify application, collected from the Google Play Store between January and July 2022. Its primary purpose is to facilitate the analysis of user sentiments and feedback regarding the app. Each review has been carefully labelled as either "Positive" or "Negative" based on its sentiment, offering a clear basis for sentiment analysis. The collection is well-documented, well-maintained, and contains clean data, making it a valuable resource for understanding user experience and satisfaction.

Columns

The dataset contains user reviews and their corresponding sentiment labels. While specific column names are not detailed in the available information, it can be inferred that it includes columns for the review text itself and a sentiment classification (e.g., 'Review_Text', 'Sentiment_Label').

Distribution

The dataset comprises over 51,000 user reviews. Data files are typically in CSV format. The sentiment distribution within the dataset is notable: 56% of reviews are labelled as Positive, while 44% are Negative. The exact number of rows or records is more than 51,000.

Usage

This dataset is highly versatile and can be applied to various analytical tasks. Ideal applications include sentiment analysis to gauge public opinion, trend analysis over time to observe shifts in user perception, and feature extraction to pinpoint specific aspects of the Spotify app that users commend or criticise. It is particularly useful for gaining deeper insights into user experience and satisfaction, and for identifying areas where the application could be improved. It has been used for learning, research, and application development.

Coverage

The data was collected from the Google Play Store, making its regional coverage global, reflecting reviews from users worldwide. The time range for the reviews spans from January to July 2022. The scope focuses exclusively on user feedback pertaining to the Spotify application.

License

CC-BY

Who Can Use It

This dataset is particularly beneficial for researchers and developers seeking to explore user perceptions and pinpoint areas for application enhancement. It is also suitable for individuals and organisations engaged in learning, academic research, and the development of various applications, especially those involving text analysis or machine learning.

Dataset Name Suggestions

• Spotify App User Sentiment Reviews
• Google Play Spotify Reviews 2022
• Spotify User Feedback Dataset
• Spotify Mobile App Sentiment Analysis Data

Attributes

Original Data Source: Spotify User Reviews

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Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SPOT Spotify Technology S.A. Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Bulgarian popfolk as a phenomenon

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Bulgarian pop-folk (hereinafter referred to as chalga) is a dance genre, stemming from ethno-pop, with strong hints of Oriental rhythms and instrumentals. Chalga is one of many branches of Balkan folk throughout the peninsula (turbofolk in Serbia, manele in Romania etc.) After the fall of communism in 1989 in Central and Eastern Europe, chalga rapidly found place in everyday life.
Chalga relies on provocativity, and tracks commonly contain sexually explicit lyrics. Because of this, it causes much controversy in society and there is sparse scientific work in the field. Nevertheless, chalga becomes an increasingly popular musical style. As such, we believe it must be subject to development. Finding its 'evolution' constitutes the main scientific motivation behind this study.

Choice of data

Payner LTD is a Bulgarian record label and production studio, founded in 1990. It is currently considered the largest record label in the country, producing mainly in both Bulgarian folk and chalga genres. The company has active presence in television, taking ownership of three channels: 'Planeta TV', 'Planeta Folk' and 'Planeta HD'.
Payner LTD also maintains activity in the Internet, particularly in YouTube. Their main channel in YouTube, 'PlanetaOfficial', publishes music content exclusively. 'PlanetaOfficial' can be also credited with holding the largest audience in Bulgaria - for the time being, it has got 2.1 million subscribers and 5.0 billion total video views, dominating on the national YouTube scene.
The top three YouTube accounts in Bulgaria, associated with chalga music, as of 4 Jan 2021, are: - PlanetaOfficial (Payner LTD), 2.12m subscribers, 4962m total views, - FEN TV, 0.76m subscribers, 699m total views, - Diapason Records, 0.53m subscribers, 580m total views
In all of those circumstances, 'PlanetaOfficial' was recognised as a pivotal source of data in the study.

Method

Data were acquired from MILKER, software specifically designed for this purpose.

Data

Content

The following data in payner.csv contains Spotify information of 679 resolved tracks, out of 638 detected in PlanetaOfficial, in the period 2014-2020. Every row is a track, and contains: - the unique Spotify ID of the song; - pre-processed names of the first three artists in a song (if such are present), according to their order of mention; - name of the track; - datetime of the video upload in PlanetaOfficial; - various Spotify audio features.

Purity

There are no missing values in this data. However, [MILKER] is not flawless. It includes tracks, not associated with PlanetaOfficial - for example works by Bach and Beethoven. In this context, data purity is defined as the fraction of songs with corresponding video uploads by PlanetaOfficial.

After random sampling (n=100), it may be inferred that the purity of this dataset is (0.91, 0.99), 95% C.I.

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Spotify's (SPOT) Growth Potential Fuels Bullish Analyst Outlook.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Wiki-MID Dataset Wiki-MID is a LOD compliant multi-domain interests dataset to train and test Recommender Systems. Our English dataset includes an average of 90 multi-domain preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million Twitter users traced during six months in 2017. Preferences are either extracted from messages of users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their "topical" friends, i.e., followees representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles describing them. This unique feature of our dataset provides a mean to categorize preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others. Data model: Our resource is designed on top of the Semantically-Interlinked Online Communities (SIOC) core ontology. The SIOC ontology favors the inclusion of data mined from social networks communities into the Linked Open Data (LOD) cloud.We represent Twitter users as instances of the SIOC UserAccount class.Topical users and message based user interests are then associated, through the usage of the Simple Knowledge Organization System Namespace Document (SKOS) predicate relatedMatch, to a corresponding Wikipedia page as a result of our automated mapping methodology.

The most successful music streaming service in the United States was Apple Music as of September, with the most up to date information showing that 49.5 million users accessed the platform each month. Spotify closely followed, with a similarly impressive 47.7 million monthly users.

What is a music streaming service?

Music streaming services provide their users with a database compiled of songs, playlists, albums and videos, where content can be accessed online, downloaded, shared, bookmarked and organized.

The music streaming business is huge, and has sometimes been lauded as the savior of the music industry. The biggest two services are in constant competition for the monopoly of the market. Apple Music was launched in 2015, whereas Spotify has been around since 2008. Other popular streaming services include Deezer, SoundCloud and iHeartRadio.

Do artists make a lot of money from streaming services? 

In short, unfortunately not. Both Apple Music and Spotify have been frequently criticized for the tiny royalty payments they offer artists. Particularly for emerging talent, streaming services are far from a lucrative source of income. Bigger, established stars like Taylor Swift are more likely to regularly make a good amount of money this way. But either way, a track needs to go viral or be streamed several million times before it earns any real cash.

At the end of each year, BillBoard compiles a playlist of the songs streamed most often over the course of that year. This year's playlist (Top Tracks of 2019) includes 100 songs. The question is: What do these top songs have in common? Why do people like them?

Original Data Source: The audio features for each song were extracted using the Spotify Web API and the spotipy Python library. Credit goes to Spotify for calculating the audio feature values.

Inspiration

• See which features correlate the most