Transformer-based recommender systems

Transformer models learn sequences. Learning language sequences is a great place to start considering the billions of messages posted on social media and cloud platforms each day. Consumer behaviors, images, and sounds can also be represented in sequences.

In this section, we will first create a general-purpose sequence graph and then build a general-purpose transformer-based recommender in Google Colaboratory. We will then see how to deploy them in metahumans.

Let’s first define general-purpose sequences.

General-purpose sequences

Many activities can be represented by entities and links between them. They are thus organized in sequences. For example, a video on YouTube can be an entity A, and the link can be the behavior of a person going from video A to video E.

Another example is a bad fever being an entity F, and the link being the inference a doctor may make leading to a micro-decision B. The purchase of product...