This repo contains supporting code for the AWS blogpost series - "Powering Recommendation and Search using IMDb Knowledge Graph". This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations and search application using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package.
This package 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.
This blog consists of 3 parts
- Graph creation - where we will create graph nodes and edges files from the IMDb and Box Office Mojo Movies/TV/OTT dataset.
- Embedding generation using NeptuneML - where we will load the graph file into Amazon Neptune, process them and train a GNN model using NeptuneML.
- Out of catalog search application - where we explain what out-of-catalog search, create Amazon OpenSearch cluster & index, and simulate an example with a local streamlit app for illustration.
A detailed walkthrough of how to run this repo is discussed in the blogposts.
This sample code is licensed under the MIT-0 License. See the LICENSE file.
For blog 3, this blog has supressed some CDK-NAG issues that we found. For production, please make sure you solve these issues.