Recommender systems play a crucial role in alleviating information overload by providing personalized recommendations tailored to users' preferences and interests. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for recommender systems, leveraging their ability to effectively capture complex relationships and dependencies between users and items by representing them as nodes in a graph structure. In this study, we investigate the environmental impact of GNN-based recommender systems, an aspect that has been largely overlooked in the literature. Specifically, we conduct a comprehensive analysis of the carbon emissions associated with training and deploying GNN models for recommendation tasks. We evaluate the energy consumption and carbon footprint of different GNN architectures and configurations, considering factors such as model complexity, training duration, hardware specifications and embedding size. By addressing the environmental impact of resource-intensive algorithms in recommender systems, this study contributes to the ongoing efforts towards sustainable and responsible artificial intelligence, promoting the development of eco-friendly recommendation technologies that balance performance and environmental considerations. Code is available at: https://github.com/antoniopurificato/gnn_recommendation_and_environment.

翻译：推荐系统通过提供符合用户偏好与兴趣的个性化推荐，在缓解信息过载方面发挥着关键作用。近年来，图神经网络（GNNs）已成为推荐系统领域一种前景广阔的方法，其通过将用户和项目表示为图结构中的节点，能够有效捕捉两者间复杂的关联与依赖关系。本研究探讨了基于GNN的推荐系统对环境产生的影响——这一维度在现有文献中尚未得到充分关注。具体而言，我们针对推荐任务中GNN模型的训练与部署过程所关联的碳排放进行了全面分析。通过考量模型复杂度、训练时长、硬件配置及嵌入维度等因素，我们评估了不同GNN架构与配置方案对应的能耗及碳足迹。本研究通过揭示推荐系统中资源密集型算法的环境影响，为推动可持续且负责任的人工智能发展贡献力量，促进在性能与环境考量间取得平衡的生态友好型推荐技术的开发。代码已开源：https://github.com/antoniopurificato/gnn_recommendation_and_environment。