The commodity and widespread use of online shopping are having an unprecedented impact on climate, with emission figures from key actors that are easily comparable to those of a large-scale metropolis. Despite online shopping being fueled by recommender systems (RecSys) algorithms, the role and potential of the latter in promoting more sustainable choices is little studied. One of the main reasons for this could be attributed to the lack of a dataset containing carbon footprint emissions for the items. While building such a dataset is a rather challenging task, its presence is pivotal for opening the doors to novel perspectives, evaluations, and methods for RecSys research. In this paper, we target this bottleneck and study the environmental role of RecSys algorithms. First, we mine a dataset that includes carbon footprint emissions for its items. Then, we benchmark conventional RecSys algorithms in terms of accuracy and sustainability as two faces of the same coin. We find that RecSys algorithms optimized for accuracy overlook greenness and that longer recommendation lists are greener but less accurate. Then, we show that a simple reranking approach that accounts for the item's carbon footprint can establish a better trade-off between accuracy and greenness. This reranking approach is modular, ready to use, and can be applied to any RecSys algorithm without the need to alter the underlying mechanisms or retrain models. Our results show that a small sacrifice of accuracy can lead to significant improvements of recommendation greenness across all algorithms and list lengths. Arguably, this accuracy-greenness trade-off could even be seen as an enhancement of user satisfaction, particularly for purpose-driven users who prioritize the environmental impact of their choices. We anticipate this work will serve as the starting point for studying RecSys for more sustainable recommendations.

翻译：在线购物的普及与广泛应用正对气候产生前所未有的影响，其关键参与者的排放数据已堪比大型都市。尽管在线购物由推荐系统算法驱动，但后者在促进可持续选择方面的作用与潜力却鲜有研究。主要原因之一可能是缺乏包含商品碳足迹排放的数据集。构建此类数据集虽极具挑战性，但其存在对于开启推荐系统研究的新视角、评估与方法至关重要。本文针对这一瓶颈问题，探究推荐系统算法的环境角色。首先，我们挖掘了一个包含商品碳足迹排放的数据集。随后，我们将传统推荐系统算法在准确性与可持续性这两个一体两面的维度上进行基准测试。研究发现：为准确性优化的推荐系统算法会忽视环保性；较长的推荐列表更环保但准确性更低。进而，我们证明采用考虑商品碳足迹的简单重排序方法，能在准确性与环保性之间建立更优平衡。该重排序方法具有模块化、即用型特点，可应用于任何推荐系统算法，无需改变底层机制或重新训练模型。实验结果表明：在所有算法与列表长度下，微小的准确性牺牲即可显著提升推荐环保性。这种准确性与环保性的权衡甚至可视为用户满意度的提升，尤其对那些以选择的环境影响为首要考虑的目标驱动型用户而言。我们预期本工作将成为研究促进可持续推荐的推荐系统的起点。