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Music Streaming Services & Recommendation Engine – The Relation is Deeper 10 June 2021

recommendation engine

Is it Monday?

Are you a regular Spotify user?


Then open your Spotify app now and go to the “Playlists” section! And come back to this destination again to know what’s happening Behind the Scenes…

Did you see – “Discover Weekly”!

Now, what’s that?

Well, it is the art of the music recommendation system that is working behind the Music Streaming App.

Isn’t it amazing to know how Spotify understands you so well!

Do you know or have you ever wondered how Spotify is doing that! It is not a human being you are talking to every day which is making it understand your taste in music, right! Then what is happening exactly!

Well, to be very precise and to-the-point, it is the technology of Artificial Intelligence working behind Spotify’s most accurate recommendation system. And it is not only limited to Spotify, whatever Audio streaming service you are using, works with recommendation system for the listeners to enjoy a better and friendly experience.

The music industry has been facing constant changes since the time of inception. From live on-stage performance to tapes, cassettes, CDs, MP3s and now, digitization – the transition is never-ending.

Back in the year 2000, the Recording Industry Association of America estimated the music industry to reach its peak with a growth of $21.5 billion. But surprisingly, the enhancement started declining.

But from 2005, the graph began going upward with the rising mass adoption of smartphones and music streaming apps. And for the first time in history, 2016 witnessed online music streaming to drive in the major revenue-part for the music industry.

Since then, even downloading songs to mobile devices started fading away because online streaming found its way to success. And now, for the last four consecutive years, the music industry is enjoying enormous growth because of music streaming.

Major Reason behind the Rising Popularity of Audio Streaming Platforms:

The internet has made everything handy for us. Though it is true that as the users are now able to listen to their favorite tracks whenever and wherever they want – on demand, there is something more to it.

It is the adaptation of AI to all the music streaming services.

Think about the traditional days – how we all used to search and shop for music CDs or kept downloading different tracks to the device for offline listening.

The intention was very clear – to find the tracks which we love.

And this behavior can never change because we all enjoy those things the most which matches our taste.

And AI, with the help of machine learning, has succeeded to acquire it. The machine learning model collects the user data, analyzes it, and then recommends the tracks in accordance with their taste. That is why “Discover Weekly” or any other “Recommended for You” is so relatable and personalized.

Example – If you are listening to the songs of Miley Cyrus then the recommendation system of your Music Streaming App should recommend the songs of Selena Gomez as well, to the users based on the listeners’ artist-taste.

How does the Recommendation System work for Music Streaming Services?

Collaborative Filtering

This is an approach where the machine learning algorithm collects a huge amount of data based on users’ behaviors, activities, and preferences. And then it recommends the Audio to those having similar choices.

For example, Person X likes the songs a, b, c, and d; and Person Y likes the songs a, b, g, and h. In this scenario, the machine learning algorithm will predict that X will also like g & h and Y will also like c & d because of their similar liking towards a & b.


Content-Based Filtering

This type of recommendation works based on the actual data such as:

  • The genre of the track

  • The language of the track

  • The artist etc.

It is much like the related searches which seldom fails.

Contextual Filtering

This is something different and very rarely used. Contextual filtering is when the system recommends based on the time, season, weather, or any other related factor.

And along with all the above-mentioned three approaches, music streaming services often use a hybrid method consisting of all the Three.

P.S. These three methods are the most popular ones used for recommendation engines. There are many others as well.

To Conclude:

You can realize the level of importance Artificial Intelligence holds for your Audio streaming service. Users may come to your platform after doing an internet search but the retention will be well-done when you will be able to recommend them with more personalized contents with utmost accuracy.

And Alie, being an AI-driven Recommendation Engine, with the technique of machine learning and the deep learning algorithm is helping the streaming services to recommend with sheer efficiency. The result is – Music Streaming Services achieving more and more success.

[Alie is the most advanced recommendation engine designed to be custom-integrated into any business platform such as Streaming Industry, eCommerce, Blogging, Health Care, Retail, and so on.] Dreaming of reaching the peak of growth with your Audio Streaming App? Then take a Free Trial now to believe that neither Alie nor the AI artwork is imagination, but an incredible reality.


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Ishita is a Content Writer with Muvi Marketing Team. Apart from business writing, she is also an acclaimed author of three best seller romantic thriller novels. In 2020, she got featured in The Hindustan Times, a leading news portal as an inspirational Indian author.

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