Netflix · LLM Recommendation Ranker

GenRec

Netflix's write-up of GenRec, a recommendation ranker built on an in-house foundation LLM, with the paper on arXiv as 2608.10257 (August). Member history and context are written out as natural-language prompts instead of thousands of engineered features. Phase one adapts an open-source LLM to Netflix catalogue and behaviour data; phase two post-trains it with ranking labels and reward signals. It is served on vLLM in prefill-only mode, scoring the whole candidate set in one forward pass with no decoding. Write-ups of the post report results comparable to or better than the production ranker with about 40x fewer labelled examples, context cut from about 5,000 to 1,700 tokens, and significant gains in an A/B test. The base model is not disclosed, and the Netflix blog could not be read directly.