GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems
Yankun Ren, Xinxing Yang, Xingyu Lu, Longfei Li, Jun Zhou, Jinjie Gu, Guannan Zhang · 2023
Transformer-based models have achieved tremendous success in sequential recommendation (SR), but they suffer from consuming excessive computational resources, particularly in the inference stage. Thus, developing lightweight yet effective SR models has become a frequent demand in industrial applications, which is also in line with the ideals of Green AI and Green IR. In this applied paper, we introduce GreenSeq deployed in Alipay to automatically design Green networks that can provide appropriate recommendations with lower computational consumption in SR. Specifically, GreenSeq uses a novel multi-layer search space that allows for flexible network design and a Greenness-aware loss term for balancing efficiency and effectiveness. Experiments on benchmark datasets and A/B testing show that GreenSeq performs well while using fewer resources. GreenSeq also reduces electricity and carbon emissions in Alipay.