Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures

arXiv:2404.14942v1 Announce Type: cross
Abstract: Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that involve learning schemes. A poisoning attack is where an adversary injects carefully crafted data into the process of training a model, with the goal of manipulating the system's final recommendations. Based on recent advancements in artificial intelligence, such …

arxiv attacks cs.cr cs.ir cs.lg poisoning attacks recommender systems survey systems type