A belief propagation approach for detecting shilling attacks in collaborative filtering

Authors:
Jun Zou;Faramarz Fekri
Affiliations:
Georgia Institute of Technology, Atlanta, GA, USA;Georgia Institute of Technology, Atlanta, GA, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

Citing 7
Cited 0

Shilling recommender systems for fun and profit

Proceedings of the 13th international conference on World Wide Web
Preventing shilling attacks in online recommender systems

Proceedings of the 7th annual ACM international workshop on Web information and data management
Classification features for attack detection in collaborative recommender systems

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Lies and propaganda: detecting spam users in collaborative filtering

Proceedings of the 12th international conference on Intelligent user interfaces
Statistical attack detection

Proceedings of the third ACM conference on Recommender systems
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recommender systems have been widely used in e-commerce websites to suggest items that meet users' preferences. Collaborative filtering, which is the most popular recommendation algorithm, is vulnerable to shilling attacks, where a group of spam users collaborate to manipulate the recommendations. Several attack detection algorithms have been developed to detect spam users and remove them from the system. However, the existing algorithms focus mostly on rating patterns of users. In this paper, we develop a probabilistic inference framework that further exploits the target items for attack detection. In addition, the user features can also be conveniently incorporated in this framework. We utilize the Belief Propagation (BP) algorithm to perform inference efficiently. Experimental results verify that the proposed algorithm significantly improves detection performance as the number of target items increases.