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
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Finding group shilling in recommendation system
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
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
Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Attack detection in time series for recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Proceedings of the third ACM conference on Recommender systems
Recommender Systems Handbook
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
Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ranking fraud detection for mobile apps: a holistic view
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Efficient and robust large medical image retrieval in mobile cloud computing environment
Information Sciences: an International Journal
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Shilling attackers apply biased rating profiles to recommender systems for manipulating online product recommendations. Although many studies have been devoted to shilling attack detection, few of them can handle the hybrid shilling attacks that usually happen in practice, and the studies for real-life applications are rarely seen. Moreover, little attention has yet been paid to modeling both labeled and unlabeled user profiles, although there are often a few labeled but numerous unlabeled users available in practice. This paper presents a Hybrid Shilling Attack Detector, or HySAD for short, to tackle these problems. In particular, HySAD introduces MC-Relief to select effective detection metrics, and Semi-supervised Naive Bayes (SNB_lambda) to precisely separate Random-Filler model attackers and Average-Filler model attackers from normal users. Thorough experiments on MovieLens and Netflix datasets demonstrate the effectiveness of HySAD in detecting hybrid shilling attacks, and its robustness for various obfuscated strategies. A real-life case study on product reviews of Amazon.cn is also provided, which further demonstrates that HySAD can effectively improve the accuracy of a collaborative-filtering based recommender system, and provide interesting opportunities for in-depth analysis of attacker behaviors. These, in turn, justify the value of HySAD for real-world applications.