This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package, which provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million movie, TV, and entertainment titles; and global box office reporting data from more than 60 countries. Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention.
In Part 1, we discussed the applications of GNNs, and how to transform and prepare our IMDb data for querying. In this post, we discuss the process of using Neptune to generate embeddings used to conduct our out-of-catalog search in Part 3 . We also go over Amazon Neptune ML, the machine learning (ML) feature of

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