Embeddings based recommendation system
TLDR: Embedding models open up a world of possibilities for self-hosted recommendation systems without the need for a large user base. A gift that came with Large Language Models (LLMs) is embedding models. Imagine a black box that takes text as input and converts it into a numeric representation, where similar things have similar numbers. For example, in two dimensions, we could pass the following words to an embedding model: sandwich, burger, cat, dog, hotdog....