Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
This addresses the problem of computationally expensive tuning for large-scale recommender systems, offering a more efficient solution for practitioners, though it is incremental as it builds on existing simulation and sampling techniques.
The paper tackled the challenge of tuning large-scale ads recommendation platforms with multiple continuous parameters by proposing Simulator-Guided Importance Sampling (SGIS), which combines open-box simulation and importance sampling to reduce computational costs while maintaining high accuracy in KPI estimation, achieving substantial improvements in KPIs with lower overhead compared to traditional methods.
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.