Reinforcement Learning to Rank Using Coarse-grained Rewards

Understand

Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks.

• Although supervised LTR methods based on fine-grained relevance labels (e.g., document-level annotations) have achieved significant success, their reliance on costly and potentially biased annotations limits scalability and alignment with realistic goals.
• In contrast, coarse-grained feedback signals, such as duration time and session-level engagement, are more accessible and affordable.
• Reinforcement Learning (RL) offers a promising framework to directly optimize these objectives using reward signals, but most existing Reinforcement Learning to Rank (RLTR) approaches suffer from high variance and low sample efficiency.