Spotify is one of the leading music apps which uses smart predictions and recommendation to its users. Gone are the days when we used to manually search, download and curate our playlists to suit our taste. The current era of Machine Learning and Data Science has made it possible for apps like Spotify to understand the taste and likings of the users and accordingly recommend songs and curated playlists.
By the end of this tutorial, you will have knowledge of the following:
- Spotify and its unique features
- How Spotify makes smart predictions
- The Machine Learning behind it
Table of Contents
Spotify – The Music Genie
During the early 2000s, the best and the most convenient way for downloading and listening to music was either from third-party websites or by piracy. Both of them required time and effort to first search the song, then download it. Even more, the pain was to create playlists containing the favourite songs. And those were static playlists. This meant that a playlist would remain as it is unless the user manually added or removed songs as per their liking. Not so convenient.
Another downside was from the artist’s perspective. The popular artists faced not many issues to market their new releases as they were all over the charts worldwide. But the new and independent artists faced lots of issues to get their music to a broad audience who would like the music they are creating. This meant that a lot of potentially killer artists were never able to do well or had to surrender to the hostile record companies.
Spotify changed the game. Launched in 2008 in Sweden, Spotify aimed to turn the music streaming industry into the mainstream. Today, Spotify boasts of about 345 million active users monthly. Spotify leverages Machine Learning and Data Science at its core and makes recommendations and curated playlists for its listeners based on the data it collects from their listening habits, location, age and many more.
The listeners now don’t have to spend time manually searching and downloading music of their taste. They now get playlists made especially for them. Moreover, they get exposed to new songs and artists every week which they otherwise would not have discovered. This is done using Machine Learning as well.
Not only this, but the artists also get the advantage now. The artists get the audience they would have not got otherwise. Their music gets automatically recommended to the listeners who like that type of music. So, it’s a win-win! Now, let’s see how Machine Learning models are leveraged.
How Does Spotify Leverage Machine Learning and Data Science?
Spotify offers four main features to its users by leveraging Machine Learning. These include:
- Home Page Playlist: It is the playlist recommendation that comes up on the home page as soon as the user opens the app.
- Discover Weekly: It is a weekly playlist recommendation that is refreshed with new songs based on the listener’s taste.
- Daily Mix: It is a daily playlist that consists of the listener’s most played and liked songs.
- Time Capsule: It is a mixed playlist containing old classics and other popular retro songs.
Out of these, the Discover Weekly feature is the flagship feature that Spotify offers. It uses Machine Learning and Big Data-based models which recommend 50 new songs in a curated playlist every Monday. This has helped Spotify reach where it is today. This feature not only binds people to the app, but it also generates even more data and hence the recommendations improve over time.
For Discover Weekly, Spotify gathers a lot of user-specific data to understand the behaviour and the satisfaction with the curated playlist. It considers data like how much time the user spent on the playlist, the number of times the songs were played, the amount of time spent on the album of that song or the artist page, if the user skipped a song or not, if the user saved it to a personal playlist or not, and if the user came back to the Discover Weekly page or not. Spotify uses 3 types of models that power its Discover Weekly page:
- Collaborative Filtering: Collaborative Filtering is a key component in any recommendation system. Netflix also uses one and uses the rating system to recommend movies. Spotify, on the other hand, doesn’t use any rating system but depends on the user behaviour metrics to see whether the listener is satisfied with the recommendation or not.
- Natural Language Processing: Spotify leverages NLP to understand the language that is being used by the listeners and reviewers around the globe for the songs. Their NLP system keeps crawling the web for any text available in the form of blog posts, reviews and any other metadata that is available. The keywords are extracted and then assigned to the song as vector representations for it. Similar artists that are mentioned in the blog are also clubbed into similar artist section. The NLP system also assigns weights to certain vectors that are used multiple times in the blog for that specific artist. It also keeps a track of the trending words that are being used and their emotion/sentiment as well. It also uses word embedding techniques like Word2Vec for grouping similar songs based on their lyrics and tags associated with them.
- Audio Models: Apart from the text-based analysis, Spotify also incorporates Audio models based on Convolutional Neural Networks. This raw data helps the model cluster the song and see how near is it to the user liking. The CNN models analyse different song characteristics such as loudness, frequency, tempo, beats per minute, composition, genre, etc. Therefore, songs with similar rhythms, tone and composition will be rated high on the recommendation charts for the user.
Related: Machine Learning Models
Although Spotify has been doing very well in the recommendation space, it still needs to improve in the personalized recommendation area. The gap between the actual satisfaction of the user and what the Machine Learning model thinks satisfaction is, needs to be closed. They acquired a French startup Niland in 2017 to improve their personalization technology.
That significantly improved the performance of the recommendations making users get the songs according to their liking. Spotify might also be looking to convert it into more of a Social Media platform for sharing songs and playlists in a better way.
Also Read: Machine Learning Project Ideas & Topics
Before You Go
With more and more users signing up, the data Spotify deals with is going to increase significantly in the coming years. This not only means a better opportunity for improved recommendations but also a challenge to handle so much data. With such immense power, Spotify data will be key to the music companies and records as well to make key business decisions based on what people currently are listening to and liking. This will be a targeted music-making strategy to maximize the listens across the users.
Spotify can also transform their Podcasts section to make it much better in recommending new podcasts to the listeners. Podcasts that talk on similar themes and topics can be grouped together and then used in recommendations. With growing competition from apps like Apple Music and YouTube Music, it will be interesting to see how the music tech space develops over the years.
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