• Artificial Intelligence
  • 5 min read

How Artificial Intelligence Helps Spotify Win In The Music Streaming World

how artificial intelligence helps spotify win in the music streaming world
Music is something that unites everyone around the globe. People vibe over the music without even understanding the actual lyrics. We can find a true Army in London and a person from Italy vibing over a Bollywood number. Spotify took advantage of this, and now it is one of the most trending on-demand mobile applications with 406 million global users. Have you ever wondered how Spotify always has the perfect playlist for you? It is because Spotify knows exactly how to leverage artificial intelligence. Today's trending technologies help Spotify to win the hearts of the music Streaming World along with the music lovers. Let's see how Spotify is using Big Data, AI/ML technologies for music streaming.

Data sets the Melody for Spotify

Artificial works on just one thing, and that is Data. That is enough for Spotify to rule the world. Ai in Spotify collects and stores various kinds of user data such as songs they play, keywords they search for, their location, the device they use, songs they frequently play and various other things. Gathering all this information and data Spotify leverages aggregation and sorting techniques to make their exceptional and strong songs recommendation model that integrates the machine learning techniques.
Data root of any decision made in every single department at Spotify. This data and information set the Spotify algorithm to speculate pertinent according to the content of the platform and trending discussions happening online about the music industry. This Spotify algorithm combines the generated user data as well which helps Spotify to enhance the user experience.
Let's take Spotify's discover weekly collection as Spotify big data case study. It has more than 40 million listeners in the first year of its introduction. The album contains 30 songs that change every Monday for each and every user. Along with that, the suggested playlist includes tracks that users might not have heard ever before but it perfectly suits their choice of songs as the playlist algorithm also includes the users' search history and potential music proposals.
The usage of the data science methodology of Spotify empowers the recommendations to work on throughout some stretch of time. That is beneficial for both users and even Spotify as Users will find various songs and artists they would like to hear and Spotify will get the users more than often.

The Discover Weekly Personalized Music Works on 3 Model:

1. Collaborative Filtering
2. NPL (Natural language Processing)
3. Audio Models

1. Collaborative Filtering

Collaborative filtering involves users' behavioral trends such as the most played songs, recently played songs, the artist's page they have visited the most, the songs of an artist the users have played most, the songs they have saved in their playlist and more. With the help of AI/ML Spotify applied a collaborative filtering approach and presented the Discover weekly playlist. Apart from that, Spotify also offers the songs' recommendations based on that as well.

2. NPL (Natural language Processing)

NPL means to understand human speech via text. It tracks the various forms of data related to the music industry such as the trending artist or music online, songs they have searched for, language being used and various other things. Spotify, later on, specifies the terms, noun, phrases and other text associated with those songs or artists and recommends the list of songs based on that. Spotify Does not have a specific library but the trending technology usage like AI, ML, Big Data in Spotify identifies the music terms for various languages and of course, it also filters out the non-music terms.

3. Audio Models

Audio Models helps Spotify to recommend the songs or artists that are not buzzing on the internet whatsoever. What if a new artist releases a new song or even launches the first-ever song on Spotify that fits perfectly to the users' recommendations list? Here comes the Audio Model, it filters the songs that are not trending in the online world, analyzes the track and recommend to the users along with the popular songs they listen to. It helps users to find new amazing songs that suit their music taste, helps new artists to reach out to potential listeners and helps Spotify to gain more artists as well as users.

Spotify - Omniscient User Preferences

Have you ever wondered why Spotify always knows everything about user preferences? Whether we talk about the special playlist recommendation for users or Discover Weekly or even a Release Radar, it always presents the list of songs that we almost fall in love with. Spotify manages to do this leveraging all today's trending technologies such as Artificial Intelligence, API integration, blockchain, machine learning and even Big data. Spotify went for acquisition with a French Startup firm in 2017. This company presented an API that provides music search and discovery algorithms leveraging deep learning and machine learning. This API helps to generate and recommend better music playlists and constantly improvises the list based on the users' preferences.
Earlier that year in April 2017 Spotify acquired another company named Mediachain Labs which used to be a Brooklyn based blockchain development company. As Spotify has millions of Artists registered on the platform, they leverage the blockchain-based algorithm to smoothen out the payment process for the artist for every track played on Spotify. Blockchain concepts are the most helpful when there is a need to keep track of every song played and pay the royalty amount based on that.


After learning all this, I don't think you will be surprised to know about Spotify's merger with the top data science company in the USA named Beats' Analytics in June 2015. Thus being said there is not even one industry that exists that won't need the assistance of a big data analytics company. A top Data science company can prove its importance no matter how small or big the project requirements can be. Reach out to HData Systems experts with your requirements and we will guide you further on how your business can achieve the unimaginable milestone with the help of nextgen tools and technologies.

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Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.

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