Summary of Combining Open-box Simulation and Importance Sampling For Tuning Large-scale Recommenders, by Kaushal Paneri et al.
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
by Kaushal Paneri, Michael Munje, Kailash Singh Maurya, Adith Swaminathan, Yifan Shi
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 addresses the challenge of tuning a large-scale ads recommendation platform by proposing a hybrid approach called Simulator-Guided Importance Sampling (SGIS). The platform has multiple continuous parameters that influence key performance indicators (KPIs), and traditional methods like open-box Monte Carlo simulators are computationally expensive. SGIS combines open-box simulation with importance sampling (IS) to reduce computational costs while maintaining high accuracy in KPI estimation. It first performs a coarse enumeration over the parameter space to identify promising initial settings and then uses IS to iteratively refine these settings. The authors demonstrate the effectiveness of SGIS through simulations and real-world experiments, showing that it achieves substantial improvements in KPIs with lower computational overhead compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out how to make a big computer system work better. You have many things you can change to make it work better, but you don’t know which changes will make the biggest difference. This paper proposes a new way to solve this problem called Simulator-Guided Importance Sampling (SGIS). It’s faster and more accurate than other methods because it uses two different techniques together. First, it looks at many possible combinations of settings to find the best starting point. Then, it makes small changes to that setting to see how well it works. This approach is better than other methods because it saves time and effort while still giving good results. |
