Robust Sybil attack defense with information level in online Recommender Systems

• Leveraging enough information against Sybil attack on the general purpose RSs.
• Extensive experiments with real world data sets.
• Achieving significant performance improvements in terms of HR and PS.

As the major function of Recommender Systems (RSs) is recommending commercial items to potential consumers (i.e., system users), providing correct information of RS is crucial to both RS providers and system users. The influence of RS over Online Social Networks (OSNs) is expanding rapidly, whereas malicious users continuously try to attack the RSs with fake identities (i.e., Sybils) by manipulating the information in the RS adversely. In this paper, we propose a novel robust recommendation algorithm called RobuRec which exploits a distinctive feature, admission control. RobuRec provides highly trusted recommendation results since RobuRec predicts appropriate recommendations regardless of whether the ratings are given by honest users or by Sybils thanks to the power of admission control. To demonstrate the performance of RobuRec, we have conducted extensive experiments with various datasets as well as diverse attack scenarios. The evaluation results confirm that RobuRec outperforms the comparable schemes such as PCA and LTSMF significantly in terms of Prediction Shift (PS) and Hit Ratio (HR).