mlsanityData to 7 Oct 2026

LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries

arXiv 2605.09794 ↗First posted 10 May 2026cs.IRcited by 1

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete picture of the user, as competitive incentives, legal constraints, user privacy concerns, and epistemic limits create persistent data barriers. This paper argues for a shift from platform-centric personalization to user-governed personalization, where only the user can integrate fragmented contexts across platforms and the offline world. The key asymmetry lies in data access: only users can aggregate their own cross-platform and offline information. Large language model (LLM) agents make such integration practically feasible for the first time by enabling reasoning over heterogeneous personal data and transforming users' cross-context information into actionable personalization capabilities. We provide proof-of-concept evidence that users equipped with cross-platform data exports and an off-the-shelf LLM agent can outperform single-platform personalization baselines. We conclude by outlining a research agenda for building scalable user-governed personalization systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 15 of its 47 references that are in the map. First posted on arXiv 10 May 2026.

Builds on

15 of 47 references.

References · 15

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2016Wide & Deep Learning for Recommender Systemscited by 745
  3. 2023A Survey on Large Language Models for Recommendationcited by 378
  4. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  5. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  6. 2024Personalization of Large Language Models: A Surveycited by 100
  7. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 99
  8. 2021Cross-Domain Recommendation: Challenges, Progress, and Prospectscited by 74
  9. 2023Federated Learning of Gboard Language Models with Differential Privacycited by 64
  10. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 32
  11. 2024User Modeling and User Profiling: A Comprehensive Surveycited by 29
  12. 2022The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimizationcited by 28
3 more
  1. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  2. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  3. 2024Federated Learning and Free-riding in a Competitive Marketcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shapecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents
  2. 2025MAP: Multi-user Personalization with Collaborative LLM-powered Agents
  3. 2025Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles
  4. 2026APeB: Benchmarking Personalization Ability of Large Language Model Agents
  5. 2026The Next Paradigm Is User-Centric Agent, Not Platform-Centric Service
  6. 2026Privacy Control in Conversational LLM Platforms: A Walkthrough Study
  7. 2026PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
  8. 2026PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks