Over the last 10 years, the way we consume online media has advanced. Whether it’s listening to music, streaming television shows, or playing our favourite games – accessibility and convenience have elevated drastically. The time it takes to click on a relevant piece of media and engage with it is as minimal as possible. This is due to a ‘preference algorithm’.

If users are checking out a collection on Netflix or scrolling through exclusive titles at RoyalPanda, preference algorithms can put their preferred choices front and centre. This helps to shorten the time it takes to find something that they might be interested in.

This is achieved through data analytics, where vast datasets are analysed to understand user behaviour and inclinations. This data is then used to cater to users on an individual level. You will probably be most familiar with this in the music streaming sector.

While in the early days of music streaming, users had to manually curate their content – creating personal playlists, finding new artists and songs, and organising their library – this curation is now done for them. This is achieved through a combination of user data, metadata, and machine learning technology.

A Tech-Driven Music Listening Experience

While browsing a platform like Spotify or Apple Music, your user data is collected and your musical preferences are constantly analysed. This will include what songs you are listening to, what songs you skip, what you like, what you rate poorly, and what you have manually inserted into a playlist.

With this data, a user profile is then built to capture your musical habits – the more you interact with the streaming platform, the more dynamic these profiles become.

Over the last few years, however, preference algorithms have been focusing on both the users and the songs that they listen to. This is where metadata analysis comes into play.

Using advanced and complex algorithms, music streaming platforms can investigate the information of every song, analysing specific attributes such as the genre, release date, tempo, and lyrics. With this metadata, it then measures patterns and similarities between tracks, and uses this affinity to suggest more to the user.

Evolving with Machine Learning

Not only do preference algorithms analyse more data than ever, but they also adapt and evolve through machine learning. With machine learning tech, music suggestions and recommendations are more accurate. Algorithms use everything from deep learning, decision trees, and clustering to understand the user and become ‘one’ with their listening habits.

These algorithms are also getting more adept at serendipity. As the user continues to interact with the platform – and their preferences continue to evolve – machine learning models can understand what they could like, rather than just what they will like. For example, if a listener plays predominantly male rap music, they could be introduced to a variety of female rappers that they wouldn’t have otherwise thought about listening to. This helps to avoid sectioning listeners into their own specific niche.

This is not only beneficial for users, but for musicians too. With most music streaming platforms housing millions of tracks and artists, it has become harder than ever to reach new listeners and get them to engage. Now, however, if listeners are engaging with similar genres and artists, they have a high chance of being suggested music in that category. They don’t have to go searching anymore – the music will be right there, already in their playlist.

The introduction of this preference algorithm helps to even the playing field and ensure that – even if they don’t have the finances to efficiently market themselves – artists can compete in the music streaming space. So long as they’re making music that certain listeners already play, they can be ‘up next’ on user playlists around the world.