mlsanityData to 7 Oct 2026

Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders

arXiv 2510.08930 ↗First posted 10 Oct 2025cs.HC cs.AIcited by 0

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Abstract (arXiv)

Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on the system's inferences. In an eight-week online field deployment with 1775 active movie recommender users, we find persistent gaps between user-perceived and system-inferred interests, show how the profile encourages engagement and reflection, and identify design directions for leveraging imperfect AI-powered user profiles to stimulate more user intervention and build more transparent and trustworthy recommender experiences.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 10 Oct 2025.

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14 of 39 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  3. 2020A Survey on Conversational Recommender Systemscited by 142
  4. 2024Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Modelscited by 51
  5. 2024MEGAnno+: A Human-LLM Collaborative Annotation Systemcited by 47
  6. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  7. 2022On Natural Language User Profiles for Transparent and Scrutable Recommendationcited by 23
  8. 2022PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagementcited by 21
  9. 2024Language-Based User Profiles for Recommendationcited by 17
  10. 2024Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysiscited by 12
  11. 2024End-to-end Training for Recommendation with Language-based User Profilescited by 10
  12. 2025LLM-based User Profile Management for Recommender Systemcited by 9
2 more
  1. 2023Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenderscited by 7
  2. 2025Recommending the right academic programs: An interest mining approach using BERTopiccited by 1

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