Earticle

초록

영어

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. However, the open nature of collaborative filtering recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Shilling attacks against numeric ratings-based CF schemes have been extensively studied. To the best of our knowledge, there is rare study about how to attack binary ratings-based recommendation systems. Hence, shilling attacks strategies against binary ratings-based recommendation algorithms need to be further investigated. The empirical results obtained from Movielens dataset show that the segment attack, which is easy to mount, affects strongly against BU-CF algorithm and BI-CF algorithm, and the attack size and the filler size don’t have the same sensitivity in attack effect.

목차

Abstract
 1. Introduction
 2. Binary Ratings based CF Algorithms
  2.1. Binary Ratings-based U-CF(BU-CF) Algorithm
  2.2 Binary Ratings-based I-CF(BI-CF) Algorithm
 3. Segment Attack Model
 4. Experiments
  4.1. Dataset and Metrics
  4.2. Attack Experimental Design
  4.3. Results and Discussion
 5. Conclusions
 References

키워드

저자

• Fuguo Zhang [ School of Information Technology, Jiangxi University of Finance & 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 Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605