Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation attack.Prior work has shown incorporating trust mechanism into traditional col...Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation attack.Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations.We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system.To the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender system.We analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the attack.Furthermore,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender system.Feasibility study of the defend method is done with the dataset crawled from Epinions website.展开更多
基金Supported by the Foundation of Jiangxi Provincial Department of Education under Grant No.GJJ.10696
文摘Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation attack.Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations.We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system.To the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender system.We analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the attack.Furthermore,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender system.Feasibility study of the defend method is done with the dataset crawled from Epinions website.
