Part 5/12:
To surpass these limitations, the researchers turned to Transformers, specifically Google's T5 (Text-To-Text Transfer Transformer). T5's architecture is renowned for excelling in NLP tasks like translation, summarization, and question answering by treating every problem as a text-to-text task, which in this context, cleverly translates to a sequence-to-sequence recommendation problem.
The core idea: Analogize sequential purchase data to natural language sequences. Just as words form sentences, products form baskets. A sequence of baskets over weeks can be seen as a paragraph, where the next basket (the target) is predicted based on the preceding sequence.