Building a Real-time Recommendation Engine With Neo4j - Part 1/4 - William Lyon - OSCON 2017 

All data and code available here:
neo4j.com/sandb...
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Description
William Lyon demonstrates how to build a recommendation engine using Neo4j and Python. The solution will be a hybrid that makes use of both content-based and collaborative filtering to come up with multilayered recommendations.
William walks you through building the solution from scratch, explaining the decisions made along the way and sharing the factors that might lead to better recommendations for the end user. You’l learn how to model the data as a graph, explore data import with Neo4j, and use the Cypher query language to write real-time recommendation queries. You’ll also make use of Python data science tools to leverage graph algorithms and natural language processing techniques to enhance your recommender system.
What you'll learn
Explore the property graph data model
Discover how to import and query data using Cypher, the query language for graphs
Learn how to create a system capable of generating real-time personalized recommendations based on user data and how to use data science tools to enhance the model used for recommendations