Collaborative Retrieval for Conversational RecSys

Recommender systems have a split personality problem.

On one side, you’ve got LLMs that can hold a conversation — parse nuance, understand when someone says “something like Inception but weirder” and actually get it. On the other, you’ve got collaborative filtering: decades of behavioral data showing that users who liked X also liked Y. Both are powerful. Neither talks to the other.

CRAG fixes that.

It’s a joint effort from University of Virginia’s VAST LAB, Cornell, and Netflix (published at WWW 2025). CRAG stands for Collaborative Retrieval Augmented Generation — the first conversational recommender system that actually combines LLM context understanding with collaborative filtering retrieval. Not in a hand-wavy “we use both” sense; in a structured, two-step mechanism that pulls collaborative filtering knowledge into the LLM’s prompt at inference time.