The recommender system behind Facebook Reels now incorporates direct user feedback through the User True Interest Survey (UTIS). By converting in‑feed survey responses into a lightweight alignment layer, the platform surfaces niche, high‑quality videos, raising engagement, retention, and overall satisfaction while addressing data sparsity and bias.
Motivation for Survey‑Driven Recommendation
Traditional signals such as likes, shares, and watch time provide noisy proxies for true user interest. Over‑reliance on these metrics can amplify short‑term popularity at the expense of long‑term relevance. The UTIS approach captures explicit interest judgments, enabling the system to distinguish fleeting curiosity from genuine preference, which is essential for sustained user loyalty.
Design of the UTIS Data Collection Framework
Each day, a random subset of viewers encounters a single‑question prompt-How well does this video match your interests?-displayed on a 1‑5 scale. Responses are weighted to correct for sampling and non‑response bias, producing a calibrated dataset that reflects real‑time user sentiment across diverse cohorts.
Model Architecture and Integration
The core ranking engine remains a large multi‑task, multi‑label model. A UTIS alignment layer sits atop the existing prediction pipeline, consuming the main models scores along with engineered features. The layer outputs a probability of interest match, which is then fed back into the ranking funnel as a boost or demotion factor.
Training Process and Feature Engineering
Survey responses are binarized to simplify learning and reduce variance. Feature engineering expands beyond engagement metrics to include content attributes (audio style, production quality, mood) and user behavior signals (session length, scroll velocity). The objective function directly optimizes for the binary interest label, improving predictive fidelity.
Offline and Online Evaluation Results
Offline analyses show a lift of over 12% in precision‑recall curves compared to the baseline. Online A/B tests report a 4.7% increase in tier‑0 retention and a measurable rise in user‑reported satisfaction. These gains validate the hypothesis that explicit feedback can enhance recommendation quality without sacrificing scalability.
Addressing Sparse Data and Bias
Users with limited interaction histories benefit from the survey signal, which provides an immediate preference cue. Bias mitigation techniques-such as stratified sampling and post‑hoc re‑weighting-ensure that under‑represented groups are not systematically disadvantaged, fostering a more equitable content ecosystem.
Future Directions with Large Language Models
To further enrich interest modeling, the team is exploring large language models for granular content understanding, including nuanced audio cues and thematic subtleties. Coupling these representations with the UTIS framework could yield even more diverse and personalized video streams.
Engineering teams often differentiate between product and platform responsibilities, as discussed in the product vs platform engineering guide, informing how the UTIS layer is deployed at scale.