个性化推荐系统描述文件攻击检测方法

Inspection Method of the Attack on Personalized Recommendation System Description File

个性化推荐系统能产生针对性的、个性化的信息来满足不同用户需求,但也很容易受到用户描述文件注入恶意攻击,影响正常的推荐结果。针对该问题,分析和研究了描述文件的形式化模型、描述文件的属性及分类方法,应用粗糙集理论,设计了数据预处理离散化、决策表约简和个性化推荐处理相应算法,提出了一种用户描述文件分类学习和攻击检测的方法;为降低攻击对推荐结果的影响,完善了推荐系统的安全,设计出一种动态交互的个性化推荐模型框架。实例证明,用户描述文件的属性分类及检测方法是有效的,准确率高,能够有效地改善个性化推荐系统模型的安全。

Personalized recommendation system can satisfy the users＇ demand with pertinent and personalized information, but it is easy to be attacked maliciously by the description file, which will influence the recommendation result. The attribute, model, and classification method of the description files are analyzed and studied. The rough set theory is used to design an algorithm of data pretreatment discretization, decision table reduction, and personalized recommendation treatment. The method of description file classification and attack detection is proposed. The safety of the recommendation system is improved to decrease the influence of the attack on the recommendation results. The frame of personalized recommendation model with dynamic interaction is considered. The example verification proves that the model, the attribute classification, and the detection method of the description flies are effective with high accuracy and can effectively improve the safety of the personalized recommendation system.