Use playlisting as data labeling for similarity checks, recommendations to each user. Similar to pinterest recommendations based on which songs are in playlists together.

Ask users to label songs themselves such as “chill”, “rock”, “hyperpop”, “Boom Bap”

Create recommendation playlists based on user label names

LastFM esque data tracking (artist, song title, album, play duration) are logged.

Listening trends over time (e.g., genre preferences, seasonal trends).

Each time a user plays a song on a supported platform (e.g., Spotify, iTunes), the track details (artist, song title, album, play duration) are logged.

2. User Profile Creation

• The scrobbled data is aggregated to create a detailed user profile. This includes:

  • Most listened-to artists, albums, and songs.

  • Listening trends over time (e.g., genre preferences, seasonal trends).

  • Behavioral data like skipping songs or repeating tracks.

3. Collaborative Filtering

• This approach looks for patterns in user behavior:

  • If User A and User B listen to a lot of the same artists, Last.fm assumes they have similar tastes.

  • Recommendations are generated by suggesting artists, albums, or tracks that User B listens to but User A hasn’t discovered yet.

• Collaborative filtering relies on the assumption that similar users enjoy similar music.

4. Content-Based Filtering

• Last.fm also uses metadata about songs, artists, and genres to make recommendations:

  • Tags: Last.fm has a tag-based system where users label songs with tags like "rock," "chill," "indie," etc.

  • Features: The algorithm considers attributes such as tempo, mood, and genre.

  • This method is independent of user data and focuses on the intrinsic properties of the music.

5. Hybrid Recommendation System

• To improve accuracy, Last.fm combines collaborative and content-based approaches:

  • Collaborative filtering can provide diverse suggestions based on user behavior.

  • Content-based filtering ensures recommendations remain relevant even when user behavior data is sparse.

6. Community-Driven Enhancements

• Last.fm leverages its community to refine recommendations:

  • Crowdsourced tags and playlists enhance metadata accuracy.

  • User reviews and ratings inform the algorithm about popular and niche tracks.

7. Machine Learning Models

• The core recommendation engine is often powered by machine learning:

  • Clustering: Grouping users or songs into clusters based on similarity.

  • Classification: Predicting a user’s preference for a song based on historical data.

  • Neural networks: Advanced systems may analyze complex patterns in listening habits.

8. Feedback Loop

• The system learns over time by tracking how users interact with recommendations:

  • Positive signals: If a user listens to a recommended song entirely or adds it to a playlist.

  • Negative signals: If a user skips or dislikes a track.

9. Network Effects

• Since Last.fm has a large user base, it benefits from the network effect:

  • More users mean more scrobbled data, which enhances the algorithm’s training and accuracy.