Defending shilling attacks in recommender systems using soft co‐clustering. (1st November 2017)

• Record Type: Journal Article
• Authors: Yang, Li; Huang, Wei; Niu, Xinxin
• Is Part Of: IET information security. Volume 11:Number 6(2017)
• Page Start: 319
• Page End: 325
• Publication Date: 2017-11-01
• Subjects: recommender systems -- pattern clustering -- security of data; shilling attacks -- recommender systems -- soft co‐clustering algorithm -- user propensity similarity method -- CCPS -- co‐clustering with propensity similarity model; Computer security -- Periodicals; Cryptography -- Periodicals; Computer networks -- Security measures -- Periodicals; Database security -- Periodicals; 005.8
• DOI: 10.1049/iet-ifs.2016.0345
• Languages: English
• ISSNs: 1751-8709

Abstract

Abstract : Shilling attacks have been a significant vulnerability to collaborative filtering based recommender systems recently. There are various studies focusing on detecting shilling attack users and developing robust recommendation algorithms against shilling attacks. Although many studies have been devoted in this area, few of them use soft co‐clustering and consider both labelled and unlabelled user profiles. In this work, the authors explore the benefits of combining soft co‐clustering algorithm with user propensity similarity method and present a soft co‐clustering with propensity similarity model or CCPS for short, to detect shilling attacks. Then they perform experiments using MovieLens dataset and Jester dataset to analyse it with respect to shilling attack detection to demonstrate the effectiveness of CCPS model in detecting traditional and hybrid shilling attacks and enhance the robustness of recommender systems.