Meta’s Outlines How It’s Improved Its Reels Recommendations

01/15/26
Source: Social Media Today – Latest News by Andrew Hutchinson. Read the original article

TL;DR Summary of How Meta Uses Surveys to Enhance Reels Recommendations

Meta is improving its Reels recommendation system by integrating user response surveys alongside traditional engagement data. This approach has increased alignment with true user preferences from 48.3% to over 70%. Unlike TikTok’s advanced visual recognition and psychographic profiling, Meta focuses on real-time feedback to better tailor content. Despite progress, Meta still faces challenges in matching TikTok’s highly personalized and engaging algorithm.

Optimixed’s Overview: Enhancing Social Video Feeds Through User Feedback and Algorithm Refinement

Meta’s Innovative Use of User Surveys to Improve Reels

Meta has introduced a large-scale survey system within its Reels feed, prompting users to rate their immediate reaction to videos. By weighting these survey responses to correct for biases and integrating them with machine learning, Meta has developed a more accurate dataset that reflects genuine user interests beyond typical signals like likes or watch-time.

Significant Improvement in Recommendation Accuracy

  • Before surveys: Recommendation alignment was at 48.3%.
  • After surveys: Alignment increased to over 70%, showcasing the effectiveness of direct user feedback.
  • Meta aims to further refine personalization, reduce sampling biases, and enhance diversity in recommended content.

Comparison with TikTok’s Advanced Algorithm

While Meta leverages survey data and standard engagement metrics, TikTok’s algorithm excels due to its advanced entity recognition capabilities powered by computer vision. TikTok can analyze very specific visual and contextual elements within videos, enabling it to match content more precisely with user preferences.

  • TikTok’s system reportedly identifies physical traits, backgrounds, and other visual cues to tailor the feed.
  • This granular understanding of content helps TikTok keep users engaged for extended periods.
  • However, TikTok’s deep psychological targeting raises concerns around user manipulation and bias.

Meta’s Ongoing Challenges and Future Opportunities

Meta’s reliance on surveys and common algorithmic signals currently limits its ability to match TikTok’s depth of personalization. Potential exists to incorporate psychographic data and richer user history to enhance recommendations further. As Meta continues evolving its approach, users may notice more relevant and engaging Reels content over time.