关键词: 协同过滤, 推荐系统, 欺诈攻击, 奇异值分解

Abstract: Collaborative filtering is a vital central technology in personalized recommendation，but it is so sensitive to user profiles，that shilling attackers can easily inject biased profiles in an attempt to force a system to adapt in a manner advantageous to them.Recent research shows that the model and the cost of shilling attacks have different impacts on attack performance.This paper analyzes the attack effectiveness of different attack models on a SVD-based collaborative filtering algorithm，and the performances of attack models with different fill sizes and attack sizes using three evaluation parameters.

Key words: collaborative filtering, recommender systems, shilling attacks, Singular Value Decomposition（SVD）