Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 9 4 2017 12 01 A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems 871 892 62727 10.22059/jitm.2017.237094.2088 EN Javad Nehriri MSc. Student, Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Sasan Hosseinali Zadeh Assistant Prof., Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Journal Article 2017 07 04 Recommender systems are widely used, in social networks and online stores, to overcome the problems caused by the large amount of information. Most of these systems use a collaborative filtering method to generate recommendations to the users. But, as in this method users’ feedback is considered for recommendations, it can be significantly erroneous by the malicious people. In other words, there may be some users who open fake profiles and vote one-sided or biased in the system that may cause disturbance in providing proper recommendations to other users. This kind of damage is said to be shiling attacks. If the attackers succeed, the user's trust in the recommender systems will reduce. In recent years, efficient attack detection algorithms have been proposed, but each has its own limitations. In this paper, we use profile-based and item-based algorithms to provide a new mechanism to significantly reduce the detection error for shilling attacks. https://jitm.ut.ac.ir/article_62727_93d393fb683facf47efba33c1c9a196a.pdf