Recommender systems have been used to tailor customer experience on online platforms. Amazon Personalize is a fully-managed service that makes it easy to develop recommender system solutions; it automatically examines the data, performs feature and algorithm selection, optimizes the model based on your data, and deploys and hosts the model for real-time recommendation inference. However, due to unique constraints in some domains, sometimes recommender systems need to be custom-built.
In this project, I will walk you through how to build and deploy a customized recommender system using Neural Collaborative Filtering model in TensorFlow 2.0 on Amazon SageMaker, based on which you can customize further accordingly.
Create an Amazon SageMaker notebook instance (a ml.t2.medium instance will suffice to run the notebooks for this project)
There are two notebooks associated with this project:
- data preparation notebook.ipynb
This notebook contains data preprocessing code. It downloads MovieLens dataset, performs training testing split and negative sampling, and uploads processed data onto Amazon S3. - model training notebook.ipynb
This notebook requires ncf.py file to run. It initiates a TensorFlow estimator to train the model, then deploys the model as an endpoint on Amazon SageMaker Hosting Services. Lastly, it shows how to make batch recommendation inference using the model endpoint.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
MovieLens dataset provided by GroupLens.
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872