sisinflab/adversarial-recommender-systems-survey
- 类型：github
- 标识：sisinflab/adversarial-recommender-systems-survey
- 链接：https://github.com/sisinflab/adversarial-recommender-systems-survey
- 主题：multimodal, risk
- 主分类：risk
- 形态：awesome
- 分类：academic-writing
- 学术复核时间：2026-08-11T22:01:03+08:00
- 学术证据：anchor:literature; anchor:文献; task:systematic-review:literature review
- 学术判定：deterministic
- 学术相关度：80
- 学术用途：systematic-review
- 学术主任务：systematic-review
- 学术阶段：evidence
- 学术状态：accepted
- 学术方向：summarize
- Stars：166
- 周增：+0
- 最近提交：2021-03-03
- 简介：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.
- 上次采集：2026-08-11
- 首次采集：2026-08-04
- 待LLM分类：否
- 成熟度：research
- 简介中文：本综述目标有二：(i) 综述对抗机器学习（AML）在推荐系统（RS）安全上的最新进展，即攻击与防御推荐模型；(ii) 展示 AML 在生成对抗网络（GAN）生成应用中的成功应用，得益于其学习（高维）数据分布的能力。本文对发表于主流 RS 与 ML 期刊会议的 74 篇文献进行了详尽综述，可作为 RS 社区在推荐系统安全及利用 GAN 提升生成模型质量方面的参考
- 场景：adversarial ML、recommender systems
- 来源文件：
- [GitHub Search]