分析GNN推荐系统碳排放，推动绿色人工智能发展
Eco-Aware Graph Neural Networks for Sustainable Recommendations
- 评估不同GNN架构的能耗与碳足迹
- 发现模型复杂度和训练时长显著影响碳排放
- 为可持续推荐系统提供可量化优化方向
推荐系统通过个性化内容缓解信息过载，图神经网络（GNN）因其能有效捕捉用户与物品间的复杂关系而被广泛应用。本文首次系统研究基于GNN的推荐系统环境影响，全面分析其训练与部署过程中的碳排放。通过评估不同GNN架构、模型复杂度、训练时长、硬件配置和嵌入维度下的能耗与碳足迹，揭示资源密集型算法对环境的实际负担。研究为实现高性能与低环境成本的平衡提供依据，推动可持续人工智能发展。代码已公开于：https://github.com/antoniopurificato/gnn_recommendation_and_environment。
原文摘要 · Abstract (English)
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.
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