Summary of Estimating the Hessian Matrix Of Ranking Objectives For Stochastic Learning to Rank with Gradient Boosted Trees, by Jingwei Kang et al.
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
by Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
First submitted to arxiv on: 18 Apr 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 This paper tackles a crucial gap in Stochastic Learning to Rank (LTR) by introducing the first-ever stochastic LTR method for Gradient Boosted Decision Trees (GBDTs). The authors focus on optimizing probabilistic ranking models, which enable unique qualities such as increased diversity and fairness. However, existing methods have been limited to differentiable ranking models like neural networks. To address this limitation, they develop a novel estimator for the second-order derivatives, known as the Hessian matrix, which is essential for effective GBDTs. This breakthrough allows for efficient computation of both first- and second-order derivatives simultaneously within the PL-Rank framework. The results demonstrate that stochastic LTR without the Hessian performs poorly, while the proposed method achieves competitive performance with the current state-of-the-art. This innovation brings GBDTs into the realm of stochastic LTR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn to rank things in a fair and diverse way. Right now, there are two main ways to do this: using neural networks or decision trees. The authors wanted to find a way to use decision trees, which are really good at certain tasks, but can’t handle the randomness that’s needed for fairness and diversity. They came up with a new way to calculate the extra information needed for decision trees to work well in this type of situation. This allowed them to combine the strengths of both approaches and create something new and better. The results show that their method is really good at ranking things, which could be useful in lots of different areas like search engines or recommendations. |
