원문정보
초록
영어
In view of the high vulnerability of traditional user-based recommendation algorithm to shilling attacks, In this paper, on the basis of the work of the group effect on the attack profiles, this paper analyzes the statistical features of the nearest neighbors of target users before and after attack, Design a kind of Attack Profiles online filter to attack the target user profile from the nearest neighbor filter. And this filter improves the user-based recommendation algorithm nearest neighbor selection strategy, thus proposes the Collaborative Recommendation algorithm based on Online Filter for Attack Profiles (CROFAP). Experiments show that attack profile online filter can accurately identify and filter out most attacks profile to ensure the robustness of the CROFAP algorithm.
목차
1. Introduction
2. Recommendation Algorithm based on User
3. Attack Profile Online Filter
3.1. Before and After Target of Shilling Attacks in Statistical Characteristics of User's Nearest Neighbor
3.2. The Filter Process and Interpretation
4. Experimental Analysis and Results
4.1. Data Sets and Experimental Setup
4.2. The Filtering Effect of Attack Profile
4.3. Parameter Selection
4.4. The Experimental Results
5. Conclusion
References