Semantic clustering-based attack detection model on CF-based recommender systems
基于语义聚类的协作推荐攻击检测模型

Collaborative recommender systems have been widely used in E-commerce environment. Because this recommendation technology is very sensitive to user's profile, an attacker can affect the prediction by injecting a lot of biased users' profiles. Therefore, the author proposed a semantic clustering-based attack detection model on CF-based recommender systems, which mined the potential interest combination by analyzing the semantics of items in the transaction database. The proposed model judged the truth of a user's profile by detecting the randomness in a user's data. Extensive experiments demonstrate that the proposed model can effectively detect the "profile injection" attacks in CF-based recommender system, which can significantly improve the robustness and reliability of the whole system.