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.

Date Created 2020-04-14 (4 years ago)

Commits 54 (last one 3 years ago)

Stargazers 156 (0 this week)

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RepositoryStats indexes 534,880 repositories, of these sisinflab/adversarial-recommender-systems-survey is ranked #200,767 (62nd percentile) for total stargazers, and #172,456 for total watchers.

sisinflab/adversarial-recommender-systems-survey is also tagged with popular topics, for these it's ranked: deep-learning (#3,840/7826), machine-learning (#3,578/7374), security (#2,141/3753), ai (#1,395/3141), gan (#274/495), generative-adversarial-network (#178/352), recommender-system (#117/239)

Other Information

sisinflab/adversarial-recommender-systems-survey has 2 open pull requests on Github, 1 pull request has been merged over the lifetime of the repository.

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ai aml gan recsys security collabora deep-learning future-visions personalization machine-learning literature-review recommender-system adversarial-attacks adversarial-machine-learning generative-adversarial-network

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updated: 2024-06-19 @ 02:38am, id: 255655476 / R_kgDODzz-NA