Recommendation Systems


Explore the world of music recommendation using KDB.AI, diving into how vector embeddings from both categorical and numeric music data can power a recommendation system like Spotify.

The tutorial covers loading song data from an open-source Spotify dataset, preprocessing the data, creating song vector embeddings, and storing them in KDB.AI.

Additionally, it explains how to query the database for similar songs, providing practical examples and code snippets to guide you through the process, showcasing the potential of KDB.AI for music recommendation.

Download the Jupyter Notebook and any accompanying files at the repository on GitHub.