Personalization by Completion: Collaborative Filtering in LLM Weight Space

Large language models increasingly interact with users who have different preferences, goals, and styles. Personalizing these models is difficult because a user's interaction history is often sparse: many preferences that matter for a new query may never have been observed. We view this problem as a form of collaborative filtering. Just as recommendation systems infer missing entries in a user–item matrix from patterns across users, we infer how a language model should adapt to a new user and topic from adaptation patterns learned across a population. Our key idea is to represent these patterns directly in the model's low-rank adaptation weights and compose a conditional weight update for an unobserved user–topic pair. The resulting model can personalize its behavior even when the relevant preference is absent from the user's history. Experiments across preference prediction tasks and language models show that this approach consistently improves personalization, with gains that persist even when the full observed history is available. These results suggest that personalized language models can learn not only from what a user has said, but also from what similar users reveal.