Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems

Santiago Alonso, Jesús Bobadilla, Fernando Ortega & Ricardo Moya
As the use of recommender systems becomes generalized in society, the interest in varying the orientation of their recommendations increases. There are shilling attacks strategies that introduce malicious profiles in collaborative filtering recommender systems in order to promote the own products or services, or to discredit those of the competition. Academic research against shilling attacks has been focused in statistical approaches to detect unusual patterns in user ratings. Nowadays there is a growing research area...
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