• We’ve improved personalized video recommendations on Facebook Reels by moving beyond metrics such as likes and watch time and directly leveraging user feedback. • Our newUser True Interest Survey (UTIS) model, now helps surface more niche, high-quality content and boosts engagement, retention, and satisfaction. • We’re doubling down on personalization, tackling challenges like sparse user data and bias, and exploring advanced AI to make recommendations even smarter and more diverse. • Our paper, “Improve the Personalization of Large-Scale Ranking Systems by Integrating User Survey Feedback” shares full details on this work. • Delivering personalized video recommendations is a common challenge for user satisfaction and long-term engagement on large-scale social platforms. • At Facebook Reels, we’ve been working to close this gap by focusing on “interest matching” - ensuring that the content people see truly aligns with their unique preferences.

Article Summaries:

  • Facebook Reels has upgraded its recommendation engine by incorporating direct user feedback through a new User True Interest Survey (UTIS). Rather than relying solely on likes, shares, and watch time, the system now asks viewers a single-question survey (“How well does this video match your interests?”) on a 1‑5 scale, gathering real‑time relevance data. The UTIS model, trained on thousands of daily responses, improves niche content discovery and boosts engagement, retention, and satisfaction. The update also addresses sparse data and bias issues, and the team has published a paper detailing the approach and its impact on large‑scale ranking systems.

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