A Graph-Based Movie Recommender Engine

A Graph-Based Movie Recommender Engine by Marko A. Rodriguez.

From the post:

A recommender engine helps a user find novel and interesting items within a pool of resources. There are numerous types of recommendation algorithms and a graph can serve as a general-purpose substrate for evaluating such algorithms. This post will demonstrate how to build a graph-based movie recommender engine using the publicly available MovieLens dataset, the graph database Neo4j, and the graph traversal language Gremlin. Feel free to follow along in the Gremlin console as the post will go step-by-step from data acquisition, to parsing, and ultimately, to traversing.

As important as graph engines, algorithms and research are at present, and as important as they will become, I think the Neo4j community itself is worthy of direct study. There are stellar contributors to the technology and the community, but is that what makes it such an up and coming community? Or perhaps how they contributed? It would take a raft (is that the term for a group of sociologists?) of sociologists and perhaps there are existing studies of online communities that might have some clues. I mention that because there are other groups I would like to see duplicate the success of the Neo4j community.

Marko takes you from data import to a useful (albeit limited) application in less than 2500 words. (measured to the end of the conclusion, excluding further reading)

And leaves you with suggestions for further exploring.

That is a blog post that promotes a paradigm. (And for anyone who takes offense at that observation, it applies to my efforts as well. There are other ways to promote a paradigm but you have to admit, this is a fairly compelling one.)

Put Marko’s post on your read with evening coffee list.

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