OpenAgentSkill Registry Manifest
Skill: Adversarial Recommender Systems Survey
Slug: sisinflab-adversarial-recommender-systems-survey
Category: research
Description: 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.

Agent fit:
- Decision: 33/100 Needs manual review
- Primary fit: Coding agents
- Role: Needs validation

Supply profile:
- Track: Research and knowledge work
- Scenario: RAG and knowledge
- Applicable agents: Claude Code, CLI, Codex, Cursor, Research
- Maintenance: 5y since push
- Risk: Needs review

Trust:
- Trust score: 69/100 Manual review
- Audit: 60/100 Needs review

Attribution:
- Status: Community indexed
- Source: GitHub star discovery
- Creator: sisinflab
- Claim URL: https://www.openagentskill.com/skills/sisinflab-adversarial-recommender-systems-survey#claim-this-skill

Install:
npx skills add sisinflab/adversarial-recommender-systems-survey

URLs:
- Web: https://www.openagentskill.com/skills/sisinflab-adversarial-recommender-systems-survey
- API: https://www.openagentskill.com/api/agent/skills/sisinflab-adversarial-recommender-systems-survey
- Install API: https://www.openagentskill.com/api/skills/sisinflab-adversarial-recommender-systems-survey/install
- Repository: https://github.com/sisinflab/adversarial-recommender-systems-survey