Building a real-time recommendation engine with Neo4j
William Lyon explains how to use a graph database to generate real-time recommendations using real-world data. William introduces graph data modeling and querying concepts using Neo4j and Cypher, the query language for graphs to import and query data, before demonstrating how to apply graph algorithms and NLP using Python data science tools to enhance your recommendations.
| Talk Title | Building a real-time recommendation engine with Neo4j |
| Speakers | William Lyon (Neo4j) |
| Conference | O’Reilly Open Source Convention |
| Conf Tag | Making Open Work |
| Location | Austin, Texas |
| Date | May 8-11, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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.
