Adversarial Learning for Recommendation: Applications for Security and Generative Tasks Concept to Code

Abstract

Adversarial Machine Learning (AML) has initially emerged as the field of study that investigates security issues of conventional and modern machine learning (ML) models. The objective of this tutorial is to present a comprehensive overview on the application of AML techniques for recommendation in a two-fold categorization: (i) AML for the attack/defense purposes, and (ii) AML to build GAN-based recommender models. A theoretical presentation on the topics is paired with two corresponding hands-on sessions to show the efficacy of AML application and push up novel ideas and advances in recommendation tasks. The tutorial is divided into four parts. We start by introducing a summary on state-of-the-art recommender models, including deep learning ones, and we define the fundamentals of AML. Then, we present the Adversarial Recommendation Framework, to represent attack/defense strategies on RSs, and the GAN-based Recommendation Framework, which is at the basis of novel adversarial-based generative recommenders. The presentation of each framework is followed by a practical session. Finally, we conclude with open challenges and possible future works for both applications.