ComRecycle: An Intelligent Computation Recycling Framework for Online Advertising

xisting online advertising systems generate high-quality ad recommendations through a complex online serving pipeline whenever a user's ad request arrives. However, our analyses on the display advertising system in Taobao show that this paradigm could lead to an inefficient utilization of computational resources. In this work, we propose an intelligent computation recycling framework called ComRecycle, which caches and reuses unexposed ad sets for repetitive ad requests to improve the computational resource utilization. We introduce fine-grained computation recycling strategies, and formulate the computation recycling decision as an online constrained optimization problem. Therefore, ComRecycle can achieve the goal of reducing computation costs while guaranteeing the same level of recommendation performance. Extensive offline experiments validate the correctness and effectiveness of ComRecycle. Our online A/B testing demonstrates that ComRecycle can save over 20% computational resources while maintaining the same system performance as the baseline.