Stealthy attack on graph recommendation system

Hao Ma, Min Gao, Feng Wei, Zongwei Wang, Feng Jiang, Zehua Zhao, Zhengyi Yang

Abstract: Highlights•We explore the stealthiness of recommender attack to fill this research gap.•We introduce a poisoning attack method based on a bi-level optimization framework.•We leverage contrastive learning to align item embeddings for stealthiness.•We boost attack effectiveness with minimum cost by optimizing target item embedding.•Our experiments show the stealthiness and effectiveness of our attack.