mlsanityData to 14 Oct 2026

Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections

arXiv 2608.04274 ↗First posted 4 Aug 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

Social media recommendation feeds often optimize for users' immediate impulses rather than preferences they would hold after deeper reflection. Some systems address this misalignment by incorporating users' explicit preferences via a configuration page or in-feed controls instead of just behavioral signals. However, users typically have evolving preferences, and their stated preferences and behavior naturally diverge, necessitating continuous reflection and feed realignment. But existing strategies require the user to take initiative and are often effortful; as a result, in practice they are rarely invoked. We present Compass, a system that aligns a user's feed with their reflective preferences by helping users reflect on and articulate their preferences given their behavior. To enable continuous reflection during everyday browsing, Compass surfaces in-situ reflections via lightweight notifications, while feed alignment is achieved by periodically simulating behavioral signals and directly manipulating feed content. We embedded Compass within YouTube Shorts and compared it against a baseline without continuous support through a 10-day field study (N=15). We found that Compass promoted more reflective and purposeful feed consumption, iterative preference adjustment, and stronger feed alignment, without sacrificing the casual nature of feed browsing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 90 references that are in the map. First posted on arXiv 4 Aug 2026.

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26 of 90 references.

References · 26

  1. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 311
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  3. 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 50
  4. 2022The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimizationcited by 29
  5. 2022Estimating and Penalizing Induced Preference Shifts in Recommender Systemscited by 29
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  10. 2025Interactive Recommendation Agent with Active User Commandscited by 10
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  12. 2024End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Promptingcited by 8
14 more
  1. 2024TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feedscited by 7
  2. 2024What We Know About Using Non-Engagement Signals in Content Rankingcited by 6
  3. 2024Bluesky: Network Topology, Polarization, and Algorithmic Curationcited by 6
  4. 2025Bonsai: Intentional and Personalized Social Media Feedscited by 6
  5. 2024Mapping the Design Space of Teachable Social Media Feed Experiencescited by 5
  6. 2025Alexandria: A Library of Pluralistic Values for Realtime Re-Ranking of Social Media Feedscited by 5
  7. 2024Reranking Social Media Feeds: A Practical Guide for Field Experimentscited by 4
  8. 2025Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTokcited by 4
  9. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  10. 2025Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Contentcited by 3
  11. 2021Ethical User Interfaces: Exploring the Effects of Dark Patterns on Facebookcited by 2
  12. 2025Understanding Decentralized Social Feed Curation on Mastodoncited by 2
  13. 2025PILOT: Control Surfaces for Authoring Social Media Feedscited by 2
  14. 2025"They've Over-Emphasized That One Search": Controlling Unwanted Content on TikTok's For You Pagecited by 1

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