AI BriefingGenRec: Toward Netflix's LLM-Native Recommendations
Key point
Netflix has presented a direction for transitioning its complex existing recommendation system into an LLM-native structure.
Details
Netflix's recommendation system has been operated by combining thousands of hand-crafted features about users, content, and interactions with sequence modeling, feature interactions, and multi-task learning. In the process of supporting not only movies and series but also various content types such as games, live, and podcasts, as well as multiple service screens, system complexity has grown as well.
In this structure, adding a new content type or service screen requires substantial feature engineering, architectural changes, infrastructure work, and experimentation. To reduce this expansion cost, Netflix presents GenRec, exploring the possibility of restructuring the recommendation system around LLMs.
Based on broad world knowledge and strong language understanding capabilities, LLMs can directly represent user viewing history and content metadata. Netflix introduces recent research such as PLUM, GLIDE, and OneRec-Think as related trends, explaining the possibility of a transition toward LLM-native recommendations.
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