Netflix tests language model as alternative to hand-built recommendation logic

Netflix pitted its years-old recommendation engine against a language model and says it got better results. The system, called GenRec, needed a fraction of the labeled training data the old system required.

Netflix’s current recommendation system relies on thousands of hand-crafted features about users, titles, and interactions, according to a blog post from the Netflix tech team. That complexity makes it expensive to onboard new content types like games, live formats, or podcasts, and to expand into new areas of the Netflix interface. But off-the-shelf language models aren’t ready for recommendations either. They over-index on popular content, hallucinate titles that don’t exist in the catalog, and ignore business rules. GenRec is designed to fill that gap. Netflix trains a proprietary model in two stages. First, an unnamed open-weight language model gets fine-tuned on Netflix data so it understands the catalog and user behavior. Then a second round of specialized training turns that base model into a recommendation ranker. This second stage gets updated more often to account for new titles and shifting preferences.Ad Instead of encoding user data as dense numerical vectors, Netflix converts it into plain text. Plays, watch durations, thumbs up or down, list additions, and drop-offs become a kind of dialogue between the user and the recommendation system. The model picks up on patterns like genre preferences or shifting interests on its own, rather than having them spelled out through manually engineered features.Ad A full text version of every interaction would blow past the model’s context window, so Netflix filters aggressively. High-signal events like long watch sessions stay in full detail, while brief taps or quick scrolls get dropped and binge sessions get condensed. To keep the model from suggesting titles that don’t actually exist, Netflix adds a separate component that only scores real catalog entries. GenRec runs on vLLM in a mode where the model reads the input once and scores all candidates in a single pass without generating any text. That keeps costs manageable.Ad Compared to the production system that has been tuned over many years, GenRec delivered about 1.6 percent better ranking quality offline.