Building Relevance Formulas with LLMs

“E-commerce search is often about exploring options and providing good recommendations. Say I want to buy a car and I have some hard filters (e.g. budget) but most criteria are weights: cheaper is better, more reliable is also better (these two tend to collide). I prefer orange, but other colors are OK.

I want to tell the LLM what I want - like in RAG - but I want sorted results with previews and facets - like in traditional E-commerce search - so I can see what’s available and refine my filters manually. In short, I want a chatbot instead of a search box.

This session covers a PoC of this approach. We express product attributes as tensors, then compute the score as a dot-product of these tensors and those representing user preferences. We’ll combine these dot-products with other criteria - like distance or price - into an overall score with weights that can be tuned by chatting with an LLM. Because I want a cheap AND reliable car and I’m willing to discuss what that means for me :)”

Kristian Aune

Vespa