Who is Really Affected by Fraudulent Reviews? An analysis of shilling attacks on recommender systems in real-world scenarios
arXiv:1808.07025v13 citations
Originality Synthesis-oriented
AI Analysis
This addresses the problem of fraud in recommender systems for users and platforms, but it appears incremental as it builds on existing attack analysis.
The study analyzed shilling attacks on recommender systems in real-world scenarios, quantifying their effects on algorithm performance and identifying the user types most impacted.
We present the results of an initial analysis conducted on a real-life setting to quantify the effect of shilling attacks on recommender systems. We focus on both algorithm performance as well as the types of users who are most affected by these attacks.