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受到广泛关注，这凸显了建立标准化基准以评估和比较这些系统性能的需求。与此同时，绿色AI倡导降低机器学习的能耗与环境影响。为应对这些问题，我们首次提出面向新闻推荐的绿色AI基准评测框架GreenRec，并设计了一个用于评估推荐准确性与效率之间权衡的指标。该基准涵盖30个基础模型及其变体，既包括传统的端到端训练范式，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范式在达到与最先进端到端范式相当的准确性的同时，其可持续性指标最高可提升2992%。