Who is Really Affected by Fraudulent Reviews? An analysis of shilling attacks on recommender systems in real-world scenarios

2018/08/21 by Anu Shrestha, Shrestha, Anu, Francesca Spezzano +3
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Social and Information Networks (cs.SI) #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.1808.07025

openalex publication_date 2018/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

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