You open your favorite streaming app and instead of drowning in the endless music catalogs, immediately turn on ready-made playlists with songs tailored to your specific tastes. This all is possible thanks to AI-based music recommendation systems (MRS).
In this article, we will survey such recommendation systems for music that are especially used in streaming-like music & audio services. We are going to learn the basic principles of its work from both technical and user points of view, look into its benefits and business use cases, supporting them with a real example.
For the icing on the cake, we are going to create a collaborative recommendation system by ourselves: for this, we will use a free public dataset and apply the K-Nearest Neighbors (kNN) algorithm to identify songs that you like.
This article will be useful to startups in the Music & Audio industry, especially for those whose business model revolves around streaming technologies. And if you are just an enthusiast curious about how music recommender works and probably even willing to test it in practice – keep on reading to satisfy your curiosity.
What is a music recommendation system?
Recommendation system is a filtering system, the purpose of which is to predict the preference that a user would give to a particular element, in our case – to a song. It is a core of huge engines that work by certain recommender algorithms and suggest a single item or a set of items to users based on such predictions.
Whether we are aware of it or not, a variety of recommendation systems have become an integral part of our daily routine since recently. Starting from accurately targeted advertising product suggestions, and finishing with personalized video or music playlists compiled specifically for us – recommendation systems seem to be encompassing our everyday lives from literally every corner of digital space.
A phenomenon of these days TikTok is built all around the recommendation system engine: that’s why TikTok’s algorithms are considered to be unique and are promising many more opportunities to the creators to grow organically - or in other words, with the help of recommender system algorithms.
In the music industry, recommendation systems are part of a big engine of streaming apps like Spotify, YouTube Music, Deezer, Tidal, and the like.
They serve to ensure a quality streaming experience for you.
How does a music recommendation system work?
There are 2 most popular recommendation systems:
- content-based (recommendations based on the similarity of content or, in our case — attributes of two songs)
- collaborative (recommendations based on similarity of users’ preferences and using matrices with ratings for each content piece, in our case — a song)
The content-based approach relies on the similarity of particular items. While using a streaming music service, a user puts likes or dislikes on the songs, creating playlists. The main idea of a content-based recommendation system is to extract keywords from the description of a song that a user likes, compare them with the keywords from the other songs, and, based on this, recommend similar songs to the user.
In turn, a collaborative system is built on the basis of users’ overlapping preferences and ratings of songs. It assumes that if user A and user B express similar preferences, similar songs can be recommended to them, meaning that if user A likes a particular song, it is likely that this song will also appeal to user B, and vice versa. Collaborative recommendation systems are generally considered to be more accurate, as they rely on the direct user interactions with the system versus on content similarity.