sisinflab/adversarial-recommender-systems-survey
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
This resource provides a comprehensive list of research papers and tutorials on how adversarial machine learning impacts recommender systems. It helps researchers, data scientists, and engineers understand both how to protect recommendation models from malicious attacks and how to use generative adversarial networks (GANs) to improve recommendation quality. The resource takes in the latest research publications and outputs a categorized, up-to-date table of relevant literature.
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Use this if you are a researcher or practitioner building, securing, or enhancing recommendation engines and need a curated overview of adversarial machine learning techniques in this domain.
Not ideal if you are looking for ready-to-use code implementations or a high-level, non-technical introduction to recommender systems.
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Mar 03, 2021
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