As global warming soars, evaluating the environmental impact of research is more critical now than ever before. However, we find that few to no recommender systems research papers document their impact on the environment. Consequently, in this paper, we conduct a comprehensive analysis of the environmental impact of recommender system research by reproducing a characteristic recommender systems experimental pipeline. We focus on estimating the carbon footprint of recommender systems research papers, highlighting the evolution of the environmental impact of recommender systems research experiments over time. We thoroughly evaluated all 79 full papers from the ACM RecSys conference in the years 2013 and 2023 to analyze representative experimental pipelines for papers utilizing traditional, so-called good old-fashioned AI algorithms and deep learning algorithms, respectively. We reproduced these representative experimental pipelines, measured electricity consumption using a hardware energy meter, and converted the measured energy consumption into CO2 equivalents to estimate the environmental impact. Our results show that a recommender systems research paper utilizing deep learning algorithms emits approximately 42 times more CO2 equivalents than a paper utilizing traditional algorithms. Furthermore, on average, such a paper produces 3,297 kilograms of CO2 equivalents, which is more than one person produces by flying from New York City to Melbourne or the amount one tree sequesters in 300 years.

翻译：随着全球变暖加剧，评估研究的环境影响比以往任何时候都更为关键。然而，我们发现极少甚至没有推荐系统研究论文记录其对环境的影响。因此，本文通过复现一个典型的推荐系统实验流程，对推荐系统研究的环境影响进行了全面分析。我们重点估算了推荐系统研究论文的碳足迹，揭示了推荐系统研究实验的环境影响随时间演变的情况。我们系统评估了2013年和2023年ACM RecSys会议的全部79篇长文，分别分析了采用传统算法（即所谓经典AI算法）与深度学习算法的代表性实验流程。通过复现这些代表性实验流程，使用硬件能耗计量器测量电力消耗，并将测得的能耗转换为二氧化碳当量以评估环境影响。研究结果表明：采用深度学习算法的推荐系统研究论文产生的二氧化碳当量，约为采用传统算法论文的42倍。此外，此类论文平均产生3,297千克二氧化碳当量，超过单人从纽约飞往墨尔本的航班排放量，相当于一棵树300年的碳封存量。