Transformer-Based Recommender Systems

Learn about how to build transformer-based recommender systems.

We'll cover the following

General-purpose sequences
Dataset pipeline simulation with RL using an MDP
Metahuman recommenders

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 lesson, we will first create a general-purpose sequence graph and then build a general-purpose transformer-based recommender in Jupyter notebook. 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 therefore 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 D on Amazon by a consumer can generate a link to a suggestion, C, or another product. The examples are infinite!

We can define the entities in this section with six letters:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Transformer-Based Recommender Systems

General-purpose sequences