Defending shilling attacks in recommender systems using soft co-clustering

Defending shilling attacks in recommender systems using soft co-clustering

For access to this article, please select a purchase option:

Buy eFirst article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
— Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

Related content

This is a required field
Please enter a valid email address