Getting Started: Session-based Recommendation with Synthetic Data

This example notebook focuses on the following basic concepts of Transformers4Rec:

• Generating synthetic data of user interactions.

• Preprocessing sequential data with NVTabular on GPU.

• Using the NVTabular dataloader with Pytorch.

• Training a session-based recommendation model with a Transformer architecture (XLNET).

Refer to the following notebooks:

• ETL with NVTabular

• Session based XLNET: TensorFlow | PyTorch