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Evaluating user experience (UX) with automated computational methods has gained increasing attention, supported by empirical evidence from UXBench. However, binary preference prediction provides limited insight, while relying on a single user-agnostic reward model overlooks the inherent heterogeneity of users, whose ex...
This work proposes GUIDER (Generative User Interest Discovery & Explicit Reasoning), a framework that fundamentally decouples intent planning from item matching by reformulating sequential modeling within a rigorous closed-set semantic interest space.
The emergence of Large Language Models (LLMs) has driven a paradigm shift in sequential recommendation from discriminative ranking to generative modeling. However, existing generative approaches predominantly rely on semantic IDs (SIDs)—discrete identifiers derived from hierarchical quantization that function as semant...
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