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Building a Data Pipeline for a Recommender System

Overview

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.

What you'll build

You will use a Jupyter notebook to build a pipeline to train a recommendation system.

What you'll learn

• 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

What you'll need

A Python environment with support of Jupyter notebook

Get the tutorial code

This tutorial code is available:

• at Github
• on Google Colab