Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates for reducing the energy consumption and environmental impact of machine learning. To address these concerns, we introduce the first Green AI benchmarking framework for news recommendation, known as GreenRec, and propose a metric for assessing the tradeoff between recommendation accuracy and efficiency. Our benchmark encompasses 30 base models and their variants, covering traditional end-to-end training paradigms as well as our proposed efficient only-encode-once (OLEO) paradigm. Through experiments consuming 2000 GPU hours, we observe that the OLEO paradigm achieves competitive accuracy compared to state-of-the-art end-to-end paradigms and delivers up to a 2992\% improvement in sustainability metrics.

翻译：近年来，新闻推荐系统在学术界和工业界均获得广泛关注，这使得构建标准化基准来评估与比较此类系统性能的需求日益迫切。与此同时，绿色AI倡导降低机器学习过程中的能耗与环境影响。针对上述问题，我们首次提出面向新闻推荐的绿色AI基准框架GreenRec，并设计了一项用于评估推荐精度与效率权衡关系的指标。本基准涵盖30个基础模型及其变体，既包含传统端到端训练范式，也包含我们提出的高效"仅编码一次"（OLEO）范式。通过消耗2000 GPU小时的实验，我们观察到OLEO范式在保持与最优端到端范式相当精度的同时，可持续性指标提升高达2992%。