The Startup
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The Startup

Beer Recommendations using Collaborative Filtering with Neo4j

In this post, I’ll outline how to use a Neo4j graph database to generate user recommendations for a data set consisting of users, products, and user ratings for those products.

For my data set I’m using a database of 30,000 different beers (pulled from brewDB’s open API), and 100 users (I asked facebook friends to rate some beers).

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