Recommendation System

What will I learn?

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.

Agenda

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.

Who is this suitable for?

Aimed at complete beginners to KDB.AI. No prior experience required but python knowledge will make it easier to follow along.

To access your own KDB.AI instance as shown in the video simply click the blue button above or else signup here.

The Jupyter notebook shown in the video is available to download here. Note, you will need to install the requirements.txt file in the repository to make sure yo have all the relevant Python packages needed to run the code.

Got a question? Ask here on our Slack channel here!