How does Spotify decide which songs to recommend?

Spotify uses a combination of sophisticated algorithms and large-scale data analysis to decide which songs to recommend to users. Their recommendation engine primarily relies on three key approaches: collaborative filtering, natural language processing, and audio analysis. **Collaborative filtering** works by analyzing the listening habits of millions of users. If many users who listen to a particular song or artist also enjoy another song, Spotify’s system identifies this pattern and suggests that song to similar users. This method helps uncover relationships between songs and listeners based on shared preferences without requiring explicit knowledge about the music’s content. **Natural language processing (NLP)** is used to analyze text data from sources such as music blogs, news articles, and metadata like track descriptions, artist biographies, and playlists. By understanding how people talk about music, Spotify can identify trends, genres, moods, or themes associated with particular songs or artists, enhancing the relevance of recommendations. **Audio analysis** involves examining the raw audio features of tracks, such as tempo, key, loudness, danceability, and energy. Spotify’s system extracts these characteristics using machine learning models to find songs with similar sonic qualities. This allows the platform to recommend tracks that “sound alike,” even if they come from different genres or are less well-known. By combining these methods with personalized user data—like your listening history, saved songs, playlist creations, and skipping behavior—Spotify tailors recommendations to your unique tastes. Additionally, Spotify continuously updates its models to reflect changing music trends and individual user preferences, ensuring fresh and relevant suggestions in playlists like “Discover Weekly” and “Release Radar.” In summary, Spotify’s recommendation engine is a complex blend of user behavior analysis, natural language understanding, and detailed audio feature extraction, all designed to help you discover music you’re likely to enjoy.