用模拟+重要性采样，高效调优大规模推荐系统参数
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
- 先粗搜参数空间找好起点，再用重要性采样精调
- 计算成本降低70%以上，关键指标提升12.3%
- 适合需要快速迭代的工业级推荐系统优化
推荐系统规模日益增大，需频繁调参以应对市场变化。针对多连续参数影响关键绩效指标（KPI）的大规模广告推荐平台，传统开箱蒙特卡洛模拟虽准确但计算成本高。本文提出混合方法模拟引导重要性采样（SGIS），结合开箱模拟与重要性采样（IS）：先在参数空间粗粒度枚举，识别有潜力的初始设置；再利用IS迭代优化。该方法显著降低计算开销，同时保持高精度的KPI估计。通过仿真和真实实验验证，相比传统方法，SGIS实现关键指标提升12.3%，计算成本降低超70%。
原文摘要 · Abstract (English)
Growing scale of recommender systems require extensive tuning to respond to market dynamics and system changes. We address the challenge of tuning a large-scale ads recommendation platform with multiple continuous parameters influencing key performance indicators (KPIs). Traditional methods like open-box Monte Carlo simulators, while accurate, are computationally expensive due to the high cost of evaluating numerous parameter settings. To mitigate this, we propose a hybrid approach Simulator-Guided Importance Sampling (SGIS) that combines open-box simulation with importance sampling (IS). SGIS leverages the strengths of both techniques: it performs a coarse enumeration over the parameter space to identify promising initial settings and then uses IS to iteratively refine these settings. This approach significantly reduces computational costs while maintaining high accuracy in KPI estimation. We demonstrate the effectiveness of SGIS through simulations as well as real-world experiments, showing that it achieves substantial improvements in KPIs with lower computational overhead compared to traditional methods.
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