Marko Rodriguez provides a step-by-step guide on building a graph-based movie recommender engine using Neo4j and Gremlin, leveraging the MovieLens dataset to explore various queries such as user preferences, co-rated movies, and genre-related recommendations. The tutorial uses Toy Story as an example to illustrate graph traversal techniques, answering questions like which users gave high ratings to specific movies and identifying highly co-rated movies shared with Toy Story. This guide aims to introduce developers to the capabilities of graph databases in solving real-world problems, providing a free resource to learn more about graph technologies for application development.