  E-commerce search and recommendation usually operate on structured data such
as product catalogs and taxonomies. However, creating better search and
recommendation systems often requires a large variety of unstructured data
including customer reviews and articles on the web. Traditionally, the solution
has always been converting unstructured data into structured data through
information extraction, and conducting search over the structured data.
However, this is a costly approach that often has low quality. In this paper,
we envision a solution that does entirely the opposite. Instead of converting
unstructured data (web pages, customer reviews, etc) to structured data, we
instead convert structured data (product inventory, catalogs, taxonomies, etc)
into textual data, which can be easily integrated into the text corpus that
trains LLMs. Then, search and recommendation can be performed through a Q/A
mechanism through an LLM instead of using traditional information retrieval
methods over structured data.
