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🧠 AI⚪ NeutralImportance 4/10
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
🤖AI Summary
Researchers propose HRL4PFG, a new interactive recommendation framework using hierarchical reinforcement learning to promote fairness by guiding user preferences toward long-tail items. The approach aims to balance item-side fairness with user satisfaction, showing improved performance in cumulative interaction rewards and user engagement length compared to existing methods.
Key Takeaways
- →HRL4PFG uses hierarchical reinforcement learning to proactively guide users toward long-tail items rather than forcing exposure through direct recommendations.
- →The framework operates through macro-level fairness target generation and micro-level real-time recommendation fine-tuning.
- →Experiments demonstrate improved cumulative interaction rewards and maximum user interaction length compared to state-of-the-art methods.
- →The approach addresses the misalignment problem between user preferences and recommended long-tail items that reduces recommendation effectiveness.
- →The research focuses on preserving user satisfaction while achieving item-side fairness in interactive recommender systems.
#artificial-intelligence#machine-learning#recommendation-systems#reinforcement-learning#fairness#algorithmic-bias#user-experience#research
Read Original →via arXiv – CS AI
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