Given the enormous number of users and items, industrial cascade
recommendation systems (RS) are continuously expanded in size and complexity to
deliver relevant items, such as news, services, and commodities, to the
appropriate users. In a real-world scenario with hundreds of thousands requests
per second, significant computation is required to infer personalized results
for each request, resulting in a massive energy consumption and carbon emission
that raises concern.
This paper proposes GreenFlow, a practical computation allocation framework
for RS, that considers both accuracy and carbon emission during inference. For
each stage (e.g., recall, pre-ranking, ranking, etc.) of a cascade RS, when a
user triggers a request, we define two actions that determine the computation:
(1) the trained instances of models with different computational complexity;
and (2) the number of items to be inferred in the stage. We refer to the
combinations of actions in all stages as action chains. A reward score is
estimated for each action chain, followed by dynamic primal-dual optimization
considering both the reward and computation budget. Extensive experiments
verify the effectiveness of the framework, reducing computation consumption by
41% in an industrial mobile application while maintaining commercial revenue.
Moreover, the proposed framework saves approximately 5000kWh of electricity and
reduces 3 tons of carbon emissions per day.

该论文提出了一个实际的计算分配框架 GreenFlow，用于工业级级联推荐系统，考虑了准确性和碳排放，通过动态原始对偶优化方法和行动链组合的奖励计算得到最佳计算分配，从而在维持商业收入的前提下降低了计算消耗约 41%，每天节省约 5000 千瓦时的电力和减少 3 吨碳排放。