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  3. Proactive Guiding Strategy for Item-side Fairness in Interac
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Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

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0.0/10

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Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

PDF: https://arxiv.org/pdf/2603.03094v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

Overall score: 3/10
Lineage: 16ef120fbf91…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

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Dimensions overall score 3.0

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Prior Work
FlexRec: Adapting LLM-based Recommenders for Flexible Needs via Reinforcement Learning
Score 3.0stable
Higher Viability
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Score 4.0up
Higher Viability
LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation
Score 6.0up
Higher Viability
Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation
Score 8.0up
Higher Viability
Bridging Semantic Understanding and Popularity Bias with LLMs
Score 7.0up
Higher Viability
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
Score 7.0up
Higher Viability
AgenticRec: End-to-End Tool-Integrated Policy Optimization for Ranking-Oriented Recommender Agents
Score 7.0up
Higher Viability
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Score 8.0up

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