A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation Systems

作者：Gao Min^*; Ling Bin; Yuan Quan; Xiong Qingyu; Yang Linda

来源：Mathematical Problems in Engineering, 2014, 2014: 162521.

DOI：10.1155/2014/162521

Personalized recommendation systems have been widely used as an effective way to deal with information overload. The common approach in the systems, item-based collaborative filtering (CF), has been identified to be vulnerable to %26quot;Shilling%26quot; attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first. The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-based kNN (k Nearest Neighbor) CF approach.

• 出版日期2014
• 单位重庆大学