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.
William Lyon is a software engineer on the Developer Relations team at Neo4j, where he works primarily on integrating the Neo4j graph database with other technologies. Previously, William worked as a software developer for several startups in the real estate, quantitative finance, and predictive API spaces. William holds a master’s degree from the University of Montana.
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