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Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user population, particularly suppressing the needs of long-tail audiences. While Large Language Models (LLMs) have been integrated into recommendation for content understanding or ranking, directly optimizing them to output a single best headline typically leads to mode collapse---converging to generic patterns that satisfy average tastes but miss specific latent intents. To bridge this gap, we introduce GESE (Generate to Explore, Select to Exploit), a f

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