Earticle

초록

영어

Despite their great adoption in e-commerce sites, recommender systems are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. In the past decade, network based recommendation approaches have been demonstrated to be both more efficient and of lower computational complexity than collaborative filtering methods, however as far as we know, there is rare research on the robustness of network based recommendation approaches. In this paper, we conducted a serious of experiments to examine the robustness of five typical network based recommendation algorithms. The empirical results obtained from the movielens dataset show that all the two limited knowledge shilling attacks are successful against the network based algorithms, and the bandwagon attack affects very strongly against most network based recommendation algorithms, especially the algorithms considering the preferential diffusion at the last step. One way to relieve the attack impact is to assign the algorithm a heterogeneous initial resource configuration.

목차

Abstract
 1. Introduction
 2. Network based Recommendation Algorithms
 3. Attack Models
 4. Experimental Evaluation
  4.1. Dataset and Metrics
  4.2. Attack Experimental Design
  4.3. Results and Discussion
 5. Conclusion and Future Work
 Acknowledgment
 References

키워드

저자

• Fuguo Zhang [ School of Information and Technology, Jiangxi University of Finance and Economics, Nanchang, China, Jiangxi Key Laboratory of Data and Knowledge Engineering, Jiangxi University of Finance and Economics, Nanchang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605