Enabling user agency in scalable content recommendations with large language models

Existing content recommender systems usually depend on centrally stored interaction histories, creating vendor lock-in and disadvantaging newer providers who lack sufficient user data. They also limit users' ability to understand, control, or edit how their preferences are represented, since profiles are learned as opaque latent vectors within provider-controlled models. We propose a user-centric alternative in which personal agents construct interpretable, editable preference profiles in natural language. Each profile item is associated with a learnable weight indicating its importance, and profiles are learned locally under full user control, laying the groundwork for high-quality personalization across multiple content providers. Recommendations are generated by matching content with weighted profile embeddings in a shared embedding space that is fine-tuned once using only content data and subsequently used by both content providers and personal agents. This design shifts profile ownership to users while maintaining the efficiency of existing recommender systems, as online recommendation reduces to approximate nearest-neighbor search. It further lowers the barrier for new providers, who only need to embed their content into the shared space — personalization naturally emerges from user-side profile embeddings optimized by personal agents to retrieve the most relevant content. Experiments on the MIND and Goodreads datasets show that our system outperforms strong baselines while providing transparency and editability — reimagining personalization as a process owned and controlled by the user.