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An Accuracy Improvement of Detection of Profile-Injection Attacks in Recommender Systems using Outlier Analysis
2015
International Journal of Computer Applications
E-Commerce recommender systems are affected by various kinds of profile-injection attacks where several fake user profiles are entered into the system to influence the recommendations made to the users. We have used Partition around Medoid (PAM) and Enhanced Clustering Large Applications Based on Randomized Search (ECLARANS) clustering algorithms of detecting such attacks by using outlier analysis. In user rating dataset, attack-profiles are considered as outliers in these algorithms. Firstly,
doi:10.5120/21737-4930
fatcat:otjfgo6mf5gyld7ryrrfxfo4oa