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Leveraging Agentic AI to Detect and Mitigate Shilling Attacks for Enhanced Robustness in Recommendation Systems
This research project explores how agentic AI—autonomous AI agents with the ability to perceive, reason, and act—can be designed and deployed within recommendation systems to proactively identify and counteract shilling attacks. These attacks involve malicious users injecting fake reviews or ratings to manipulate recommendations. The project aims to develop an adaptive, multi-agent, Agentic AI-driven model that monitors user behavior patterns, detects suspicious activities, and dynamically adjusts recommendation outputs to maintain system integrity and user trust.
Faculty Supervisor:
Rasha Kashef
Student:
Partner:
Nile University
Discipline:
Computer science
Sector:
Education
University:
Toronto Metropolitan University
Program:
Globalink Research Award
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