On the exponential value of labeled samples
Pattern Recognition Letters
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Top 10 algorithms in data mining
Knowledge and Information Systems
WSRec: A Collaborative Filtering Based Web Service Recommender System
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Proceedings of the third ACM conference on Recommender systems
COG: local decomposition for rare class analysis
Data Mining and Knowledge Discovery
Leadership discovery when data correlatively evolve
World Wide Web
Semi-SAD: applying semi-supervised learning to shilling attack detection
Proceedings of the fifth ACM conference on Recommender systems
Analysis and detection of segment-focused attacks against collaborative recommendation
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-驴 to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.