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

RecAD: Towards A Unified Library for Recommender Attack and Defense