Journal
KNOWLEDGE-BASED SYSTEMS
Volume 148, Issue -, Pages 146-166Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2018.02.032
Keywords
Collaborative filtering recommender systems; Shilling attacks; Shilling attack detection; User rating behavior; Hidden Markov model; Hierarchical clustering
Categories
Funding
- National Natural Science Foundation of China [61379116, 61772452]
- Natural Science Foundation of Hebei Province, China [F2015203046]
- Key Program of Research on Science and Technology of Higher Education Institutions of Hebei Province, China [ZH2012028]
Ask authors/readers for more resources
The existing unsupervised methods usually require a prior knowledge to ensure the performance when detecting shilling attacks in collaborative filtering recommender systems. To address this limitation, in this paper we propose an unsupervised method to detect shilling attacks based on hidden Markov model and hierarchical clustering. We first use hidden Markov model to model user's history rating behaviors and calculate each user's suspicious degree by analyzing the user's preference sequence and the difference between genuine and attack users in rating behaviors. Then we use the hierarchical clustering method to group users according to user's suspicious degree and obtain the set of attack users. The experimental results on the MovieLens 1 M and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available