Summary of Practical and Robust Safety Guarantees For Advanced Counterfactual Learning to Rank, by Shashank Gupta et al.
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
by Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach called Proximal Ranking Policy Optimization (PRPO), which provides unconditional safety in deploying ranking models without relying on specific assumptions about user behavior. This is achieved by imposing a limit on how much learned models can degrade performance metrics, thereby removing incentives for learning ranking behavior that is too dissimilar to a safe ranking model. The existing approach to safe Counterfactual Learning to Rank (CLTR) has limitations in handling trust bias and relies on specific assumptions about user behavior. The proposed PRPO method generalizes the existing safe CLTR approach to make it applicable to state-of-the-art doubly robust CLTR and trust bias. Experiments show that both novel safe methods provide higher performance than the existing safe inverse propensity scoring approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we can make sure our ranking models are safe when we put them into use. Sometimes, these models can be bad for performance if they’re not careful. The old way of making these models safer doesn’t work well in certain situations. The new approach, called PRPO, makes sure the models stay safe without assuming anything about how people behave. This means it works even when things don’t go as planned. The experiments show that this new method is better than the old one and provides a higher level of safety. |
Keywords
* Artificial intelligence * Optimization
