Work

Attack Detection Using Item Vector Shift in Matrix Factorisation Recommenders

Year: 2025

Type: article

Abstract: This article proposes a novel method for detecting shilling attacks in Matrix Factorization (MF)–based Recommender Systems (RSs), in which attackers use false user–item feedback to promote a specific item. Unlike existing methods that use either supervised learning to distinguish between attack and genuine profiles or analyze target item rating distributions to detect false ratings, our method uses an unsupervised technique to detect false ratings by examining shifts in item preference vectors that exploit rating deviations and user characteristics, making it a promising new direction. The experimental results demonstrate the effectiveness of our approach in various attack scenarios, including those involving obfuscation techniques.

Source: ACM Transactions on Privacy and Security

DOI: 10.1145/3721285

Authors: Sulthana Shams, Douglas Leith

Institution: Trinity College Dublin

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Science Foundation Ireland

Award: G6445168105