In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values. Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments. To address this, we propose RecAD, a unified library aiming at establishing an open benchmark for recommender attack and defense. RecAD takes an initial step to set up a unified benchmarking pipeline for reproducible research by integrating diverse datasets, standard source codes, hyper-parameter settings, running logs, attack knowledge, attack budget, and evaluation results. The benchmark is designed to be comprehensive and sustainable, covering both attack, defense, and evaluation tasks, enabling more researchers to easily follow and contribute to this promising field. RecAD will drive more solid and reproducible research on recommender systems attack and defense, reduce the redundant efforts of researchers, and ultimately increase the credibility and practical value of recommender attack and defense. The project is released at https://github.com/gusye1234/recad.

翻译：近年来，推荐系统已成为日常生活中无处不在的组成部分，但由于其日益增长的商业和社会价值，面临着被攻击的高风险。尽管推荐系统攻击与防御领域取得了显著研究进展，但目前仍缺乏广泛认可的基准测试标准，导致性能比较不公平且实验可信度有限。为此，我们提出RecAD——一个旨在建立推荐系统攻击与防御开放基准的统一库。RecAD通过整合多样化数据集、标准源代码、超参数设置、运行日志、攻击知识、攻击预算及评估结果，率先构建了可复现研究的统一基准测试流程。该基准设计全面且可持续，涵盖攻击、防御和评估任务，使更多研究者能够轻松跟进并为这一前景广阔的领域做出贡献。RecAD将推动推荐系统攻击与防御领域更扎实、可复现的研究，减少研究者的重复劳动，最终提升推荐系统攻击与防御的可信度与实践价值。该项目已在https://github.com/gusye1234/recad 开源发布。