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New LLM framework GESE boosts recommendation headline personalization

By PulseAugur Editorial · [1 sources] ·

A new framework called GESE (Generate to Explore, Select to Exploit) has been developed to improve headline generation for personalized recommendations. This approach decouples the process into two stages: first, an LLM explores and generates a diverse set of candidate headlines using Group Sequence Policy Optimization to cover various user interests. Second, a lightweight selector chooses the optimal headline from this pool based on real-time user signals. Deployed on a platform with over 100 million daily active users, GESE demonstrated a 2.57% increase in click-through rate and a 0.87% rise in dwell time compared to existing methods. AI

IMPACT Enhances personalized recommendation systems by improving headline relevance and user engagement.

RANK_REASON Academic paper detailing a new framework for LLM-based headline generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LLM framework GESE boosts recommendation headline personalization

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Academic paper detailing a new framework for LLM-based headline generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yi Chen, Rufeng Cheng, Qiang Xie, Tao Li ·

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

    arXiv:2609.15094v1 Announce Type: cross Abstract: 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 L…

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