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.
GenRec converts watch history and context into natural language instead of modeling them as hand-built features. | Image: Netflix
