How streaming algorithms work — The coding magic behind your favourite music playlists

Publication date: August 14, 2024

Have you ever wondered how music streaming apps seem to know the songs you want to listen to — and often shape your tastes more than your friends? Do you ever marvel that Spotify’s weekly ‘Release Radar’ just gets you? It’s all thanks to algorithms — a complex set of rules and processes that help tailor music recommendations to your tastes. 

Let’s explore how these algorithms work, and the part they play in the experience of using services like Spotify, Apple Music or TIDAL.  

What is an algorithm?

In simple terms, an algorithm is a set of instructions that a computer follows to solve a problem or complete a task. Just like a recipe provides step-by-step directions to make a dish, an algorithm gives step-by-step instructions to achieve a specific outcome.

The basics of music streaming algorithms

Music streaming algorithms analyse data to provide recommendations. The main types of data they use include:

• User data: Your listening history, likes, skips and search queries.
• Song data: Information about the songs themselves, such as genre, tempo and artist.
• User behaviour patterns: Patterns like the time of day you listen to music, how long you listen, and what types of songs you play in different situations.

By processing this data, the algorithms can make educated guesses about what music you might like next.

The role of coding in music streaming algorithms

At the heart of these algorithms is code, written by software engineers and data scientists. Let’s explore some of the coding concepts and technologies involved:

• Data collection and storage: Music streaming services collect vast amounts of data. This data is stored in databases using technologies like SQL (Structured Query Language) and NoSQL databases. Code written in languages such as Python or Java handles the extraction, transformation and loading of this data.
• Data processing: To analyse and process the data, engineers use frameworks and libraries such as Apache Hadoop or Apache Spark, which allow for efficient handling of large datasets. Code written in Scala, Java or Python is used to create data processing pipelines.
• Machine learning models: Machine learning models are at the core of recommendation algorithms. Engineers use languages like Python, along with libraries such as TensorFlow, PyTorch and scikit-learn, to build and train these models. The models learn from the data to predict user preferences and suggest songs.
• Natural language processing (NLP): NLP techniques are used to analyse song lyrics and other text data. Python’s NLP libraries are commonly used to process and extract meaningful information from text.
• Algorithm implementation: The actual algorithms, such as collaborative filtering and content-based filtering, are implemented using code. 

Collaborative filtering

Collaborative filtering makes recommendations based on the preferences of similar users. Here’s how it works:

• Finding similar users: The algorithm identifies users with similar listening habits to yours. For instance, if you and another user both listen to a lot of hip-hop, the algorithm considers you similar.
• Cross-recommendations: The algorithm looks at what songs those similar users enjoy that you haven’t heard yet. If many of these similar users like a particular song, the algorithm might recommend it to you — like getting music suggestions from friends with similar tastes as you.

Content-based filtering

Another key technique is content-based filtering, which focuses on the characteristics of the music itself rather than user behaviour. Here’s how it works:

• Analysing song features: The algorithm analyses various attributes of each song, such as genre, tempo, instrumentation and lyrics.
• Matching preferences: It then matches these attributes to songs you’ve enjoyed in the past. If you often listen to upbeat techno, the algorithm will recommend more music with similar features.

Natural language processing (NLP)

Music streaming services also use natural language processing to understand and categorise music. NLP helps algorithms analyse text data from various sources, including:

• Lyrics: By examining the lyrics of songs, the algorithm can identify themes and sentiments that you might like.
• Reviews and articles: The algorithm can also process music reviews, artist biographies, and other text data to understand more about the music and artists you might enjoy.

This is like having an extensive library of music reviews and analyses to help find the perfect song for you.

Machine learning and AI

Machine learning and artificial intelligence (AI) play a significant role in refining these algorithms. Here’s how they enhance music recommendations:

• Continuous learning: Machine learning algorithms continuously learn from new data. As you keep using the streaming service, the algorithm gets better at predicting your tastes based on your up-to-date listening habits.
• Adapting to trends: These algorithms can also adapt to broader trends. For instance, if a new genre becomes popular, the algorithm can start recommending it to users who might be interested.

Personalised playlists and discoveries

One of the most visible results of these algorithms is personalised playlists. Services like Spotify’s ‘Discover Weekly’ or Apple Music’s ‘For You’ playlists are curated using the data and techniques we’ve discussed. Here’s how they create these personalised experiences:

• Combining methods: These playlists often use a combination of collaborative filtering, content-based filtering and machine learning to choose songs.
• Exploration and familiarity: The goal is to balance new discoveries with familiar favourites. The algorithm might include some songs that are similar to your existing favourites and others that push your musical boundaries slightly.

Future directions

In the future, we can expect these algorithms to become even more sophisticated, offering even more accurate and diverse recommendations. By understanding your preferences and analysing the characteristics of millions of tracks, they help you discover new favourites, while also recommending old favourites, like an audio comfort blanket. So the next time your streaming service chooses a song that perfectly suits your mood, think of all the coding behind the scenes that’s making it happen.

P.S. Why not follow EU Code Week on Spotify  and have a listen to some of our Code Week Podcast content!