1 research outputs found
RecAD: Towards A Unified Library for Recommender Attack and Defense
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