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When History Misleads: Asymmetric Margin Supervision for Instruction-Guided LLM Generative Recommendation
In instruction-guided generative recommendation, LLM-based recommenders need to balance two goals: responding to the user's current request and aligning with the preferences in their interaction history. When the two conflict, history events can override the request. We show that turning the effect
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