RobuRec: Robust Sybil attack defense in online recommender systems
Giseop Noh, Chong-kwon Kim · 2013
With the growth of Internet usage and online social networks, the online Recommender Systems are becoming popular among system users. Although the influence of the recommender systems is expanding, the possibility of residing fake identities (Sybils) from nefarious users increase due to various reasons. To mitigate the impact of such users, several approaches are proposed. However, the need for robust algorithms is still necessary regarding recommender systems since the small portion of Sybils can distort the accuracy of predictions extremely. We propose a novel robust recommendation algorithm (RobuRec) using information level and admission control. The performance of RobuRec is experimented on various recommendation datasets with all possible Sybil attacks. The evaluation result shows that RobuRec can improve prediction error by 21% and 49% compared to two comparable schemes (LTSMF [23] and PCA [24], respectively). On all datasets and against various attack strategies, in turn, our RobuRec scheme shows the best peformance in terms of prediction shift.