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Related Experiment Video

Updated: Jan 8, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

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Published on: September 28, 2018

Multi-criteria group shilling attacks.

Tugba Turkoglu Kaya¹

¹Computer Engineering Department, Ardahan University, Camlicatak, Ardahan, Turkey.

|December 11, 2025

Summary

This study enhances multi-criteria group recommender systems against shilling attacks. The novel approach improves recommendation accuracy and robustness, demonstrating significant gains in performance on key datasets.

Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Technology advances enable detailed user data collection for multi-criteria recommendation systems.
Group recommendation systems cater to collective preferences but face vulnerabilities like group shilling attacks.
Existing research lacks comprehensive robustness analysis for multi-criteria group recommenders.

Purpose of the Study:

To conduct the first comprehensive robustness analysis of multi-criteria group recommender systems.
To propose a novel shilling attack strategy adapted for multi-criteria group settings.
To evaluate the system's resilience against coordinated manipulation.

Main Methods:

Developed a novel shilling attack strategy by adapting group shilling models to multi-criteria environments.

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Last Updated: Jan 8, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

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Published on: September 28, 2018

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The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Performed a comprehensive robustness analysis on multi-criteria group recommender systems.

Utilized datasets (YM20, YM10) to experimentally validate the system's performance.

Main Results:

The proposed multi-criteria recommender system demonstrated notable robustness across datasets.
Average Hit Ratio (AvgHR) increased by approximately 12% on YM20 and 15% on YM10.
The MUP-NNZ strategy showed superior resistance to profile injection attacks, maintaining recommendation accuracy.

Conclusions:

Multi-criteria group recommender systems can be made robust against sophisticated shilling attacks.
The proposed attack strategy provides deeper insights into system vulnerabilities.
The MUP-NNZ method is effective in preserving recommendation accuracy under adversarial conditions.