Novel Approach Against Reverse Bandwagon Profile Inject Attack in Recommender Systems

摘要

Due to the simplicity and high recommending quality, collaborative filtering algorithms are the most successful recommender techniques and wildly used in e-commerce recommender systems. However, such systems are vulnerable to profile inject attack which is employed by inserting biased profiles into systems in order to influence the recommendations. In this paper, we propose a novel method against reverse bandwagon profile inject attack model. Our method is basing the standard collaborative filtering algorithms. Experiment results show that our method has better performance against reverse bandwagon attack model.