User Modeling
Overview
User modeling builds representations of people from interaction histories, queries, clicks, conversations, sensor streams, or learning outcomes so systems can predict needs and adapt recommendations, tutoring, and agents. Common approaches include sequential and graph encoders for behavioral data, latent-state and hierarchical persona models, retrieval-based or compact memory for language models, and synthetic-user simulation. Systems are evaluated on recommendation and next-action tasks, personalization benchmarks, and educational or conversational settings, with growing attention to temporal change, uncertainty, and evidence for inferred traits. A central open tension is whether these models genuinely capture individual users: synthetic personas and automatic judges can miss real preferences, while richer behavioral data raises privacy and over-inference risks.
Latest papers
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