How recommendation algorithms work and how to get into them

How Music Recommendation Algorithms Work

You’ve probably experienced this: your playlist of saved tracks gets stale, and you want to hear something new. You open Yandex Music, tap any playlist or «My Wave», and instantly get tracks in a similar genre. How do algorithms manage to pick songs that match your taste or sound alike? (Are they watching us?) Let’s find out.

What Are These Recommendation Algorithms?

A recommendation system is a set of algorithms and technologies that analyses users’ musical preferences and suggests the most relevant content.

How Do They Work in Practice?

For Emerging Artists

Algorithms will only work for you from the start if you actively pitch your release and promote it on social media or via vertical content. The algorithms will analyse:

• initial plays;
• listen-through rates (whether users finish the track);
• track likes, etc.

The more of these metrics you have, the better — algorithms will see that listeners are engaged with your content.

Other factors include:

• Artist activity on platforms: how many releases you put out and how they perform.
• Release quality and originality: algorithms evaluate these aspects.

Artists with Multiple Releases

Once you’ve released several tracks, algorithms start to understand your musical style. This means you should avoid mixing genres in your profile. For example, if you’re known for pop music, don’t suddenly release a heavy metal track. Imagine a listener expects your usual pop sound, but gets something completely different — it’s jarring.

When algorithms recognise your genre, you gain:

• higher priority in recommendations;
• enhanced audience behaviour analytics.

Combine this with social media promotion, and your reach will be excellent. If each of your releases shows steady growth and consistent performance, the recommendation system will be more favourable towards you.

In short, algorithms are tools driven by data. The higher quality and more consistent your content, the better the algorithms can recommend it. The key is to create good music and actively promote it.

Types of Recommendation Systems

1. Collaborative Filtering
   • User‑based filtering (user‑centric):
     • analyses behaviour of similar users;
     • recommends tracks based on audience preferences;
     • uses rating and listening data.
   • Item‑based filtering (content‑centric):
     • compares track characteristics;
     • suggests similar compositions;
     • considers genres, styles, and sound features.
2. Content Filtering
   • Track analysis:
     • processes audio files;
     • extracts sound characteristics;
     • categorises by musical parameters.
   • User profiles:
     • creates individual profiles;
     • analyses preferences;
     • tracks user behaviour.
3. Track Evaluation Algorithms
   • listening quality (whether the track is played to the end);
   • user reactions (likes, dislikes);
   • playlist additions;
   • repeat listens.

Typically, all three approaches work simultaneously. The recommendation process is constantly improving, offering users increasingly accurate musical suggestions.

How Algorithms Work on Different Platforms

1. Yandex Music
   • «My Wave» — personalised stream;
   • audio vectors — sound characteristic analysis;
   • behavioural analysis — tracking user actions;
   • Nitro system — promoting new tracks.

   Evaluation Metrics:

   • track listening time;
   • playlist additions;
   • likes and dislikes;
   • return frequency;
   • listener geolocation.
2. VK Music
   • integration with the social network;
   • analysis of friends’ activity;
   • tracking shares and likes;
   • comment monitoring;
   • community analysis.

   Key Factors:

   • content engagement;
   • track sharing;
   • discussion activity;
   • interaction frequency;
   • user geolocation.
3. Spotify
   • Discover Weekly — weekly playlists;
   • Daily Mix — mood‑based playlists;
   • Release Radar — new releases from favourite artists;
   • Made For You — personalised recommendations.

   Influencing Factors:

   • listening history;
   • playlist creation;
   • adding to favourites;
   • listen frequency;
   • geolocation.
4. Apple Music
   • For You — personalised page;
   • New Music Mix — new release playlists;
   • Today At Six — daily playlists;
   • Connect — music industry news.

   Evaluation Criteria:

   • audio content quality;
   • track metadata;
   • user activity;
   • content interaction;
   • geolocation.
5. SoundCloud
   • retweet analysis;
   • like tracking;
   • comment monitoring;
   • chart influence;
   • community activity.

   Important Metrics:

   • number of plays;
   • comment activity;
   • track sharing;
   • audience engagement.

Practical Tips for Artists

• Post snippets or pre‑save links — this shows interest and helps recommendations notice your track.
• Track your release statistics using analytics tools like Bandlink, VK Studio, and Apple/Spotify for Artists.
• Work with algorithms on vertical platforms like TikTok, Instagram, YouTube, and Like.
• Learn how to properly upload your track to all digital platforms.