Marko Rodriguez gives us a step by step guide to building a graph-based movie recommender engine using the publicly available MovieLens dataset, the graph database Neo4j, and the graph traversal language Gremlin.
He uses the animated movie Toy Story to demonstrate traversing the MovieLens Graph, and dives into these questions:
- Which users gave Toy Story more than 3 stars?
- Which users gave Toy Story more than 3 stars and what other movies did they give more than 3 stars to?
- How many of Toy Story’s highly co-rated movies are unique?
- Which movies are most highly co-rated with Toy Story?
- Which movies are most highly co-rated with Toy Story that share a genera with Toy Story?
- Which movies are most highly co-rated with Toy Story that share all genres with Toy Story?
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Keywords: gremlin • movie recommender engine • MovieLens • MovieLens dataset • neo4j • Toy Story
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