Workshop Website https://sisinflab.github.io/adverse2021/
Recently, research in adversarial machine learning has brought to light important potential security issues with systems that people use on a daily basis for search and discovery. While adversarial examples are well understood in computer vision tasks, the harmful effects of the malicious application of machine learning are less well-understood in information retrieval and recommendation systems. The issues include: injection of adversarial-crafted fake users, adversarial perturbation of multimedia data in training sets or background collections, and adversarial structural noise on graph structure in order to improve search and recommendation in real world environments, research is necessary that will allow us to discover, understand, and control the adverse impact of adversarial machine learning. In this workshop, we aim to bring together researchers from the fields of adversarial machine learning, information retrieval, and recommender systems to discuss recent advances and research directions that could be further exploited to broaden the frontier in the field.