Recommendation engines are among the most well known, widely used and highest-value use cases for applying machine learning. Despite this, while there are many resources available for the basics of training a recommendation model, there are relatively few that explain how to use Nexus to build a data pipeline, actually deploy these models to create a production-level machine learning eco-system for a recommender system.
You will use a Jupyter notebook to build a pipeline to train a recommendation system.
- Set up a Nexus environment.
- Query the data from Nexus using SPARQL.
- Prepare the data into a good shape for collaborative filtering.
- Perform a classical collaborative filtering algorithm - matrix factorization
- Push the training output to Nexus
- Recommend movies by querying the output from Nexus
A Python environment with support of Jupyter notebook
This tutorial code is available: