A Step-by-Step Guide to Build a Graph Learning System for a Movie Recommender | by Yu-Cheng Tsai | Sep, 2024


Built with PyTorch Geometric and using MovieLens DataSet

Towards Data Science
Photo by michael podger on Unsplash

A graph is a way to represent relationships between entities, using nodes to represent the entities and edges to represent the connections between them. This structure is versatile and can be applied to a wide range of real-world problems. For instance, I can create a simple graph with two nodes, user and movie, where node user gives (i.e. an edge relationship) node movie a rating of 4.5.

A simple movie rating graph (Image by the author)

By modeling data and relationships among them as a graph, one can capture rich and complex patterns, making graph modeling a reasonable approach for many modeling applications.

Recommender systems have become an integral part of our digital experience. Whether it’s suggesting “movies you might enjoy,” “news that matches your interests,” or “people you may know,” these systems power many of customer facing software products.

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