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This paper proposes DKG-MTI, a dual knowledge graph framework that augments LLM-based inference with structure-aware knowledge alignment to jointly predict aspect ratings and generate user intent statements from online travel reviews.
Presents SERUM, a multi-pass framework that extracts structured behavioral models of user actions and intents from raw egocentric video using hierarchical VLM annotation, reducing hallucinations and producing interpretable process models without manual annotation.
Introduces APeB, a benchmark for evaluating personalization in LLM agents, focusing on inferring user intent and preferences from raw queries and interaction histories. Finds that current models struggle with early-stage queries and that history-aware refinement can help.