RecAD: Towards A Unified Library for Recommender Attack and Defense

by   Changsheng Wang, et al.

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


RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

In recent years, there are a large number of recommendation algorithms p...

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

DeepRobust is a PyTorch adversarial learning library which aims to build...

Lib-SibGMU – A University Library Circulation Dataset for Recommender Systems Developmen

We opensource under CC BY 4.0 license Lib-SibGMU - a university library ...

Membership Inference Attacks Against Recommender Systems

Recently, recommender systems have achieved promising performances and b...

Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack

Defense models against adversarial attacks have grown significantly, but...

Benchmarking Adversarial Robustness

Deep neural networks are vulnerable to adversarial examples, which becom...

Towards Robust Recommender Systems via Triple Cooperative Defense

Recommender systems are often susceptible to well-crafted fake profiles,...

Please sign up or login with your details

Forgot password? Click here to reset