RGRecSys：一个推荐系统鲁棒性评估工具
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
摘要：
健壮的机器学习是一个越来越重要的主题，它专注于开发对各种形式的不完美数据有弹性的模型。由于推荐系统在在线技术中的普遍性，研究人员已经进行了一些鲁棒性研究，主要集中在数据稀疏性和配置文件注入攻击。相反，我们提出了一个更全面的观点，推荐系统的鲁棒性，包括多个维度-鲁棒性相对于子种群，转换，分布差异，攻击和数据稀疏。虽然有几个库允许用户比较不同的推荐系统模型，但没有软件库用于在不同场景下对推荐系统模型进行全面的鲁棒性评估。作为我们的主要贡献，我们提出了一个鲁棒性评估工具包，Robustness Gym for RecSys（RGRecSys --https：//www.github.com/salesforce/RGRecSys），它允许我们快速和统一地评估推荐系统模型的鲁棒性。
英文摘要：
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys -- https://www.github.com/salesforce/RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
