基于改进匿名模型的上下文推荐系统研究

摘要

基于上下文感知的推荐系统通过引入上下文环境信息进行推荐,其中用户的隐私信息往往能够被攻击者直接或间接地获取到,造成隐私泄露.针对以上问题,在上下文感知推荐系统中融入一种改进的匿名模型.结合聚类方法将不同的敏感属性值进行分组,对多敏感属性进行匿名,在隐私保护方面,对高敏感属性信息具有较高的保护程度,有相似敏感程度的信息具有相似的保护程度.在真实数据集上的实验结果表明,该方法能在保护推荐系统中用户隐私的同时,提高推荐精确度.%Recommendation system based on context sensing through the introduction of environmental information to make recommendations,the user's privacy information can be directly or indirectly acquired by the attacker,causing privacy disclosure.To solve the above problems,an improved anonymous model is introduced into the context sensing recommendation system.Combines clustering methods to divide different sensitive attribute values in different groups,for multiple sensitive attributes anonymous,in terms of privacy protection,protection degree is higher on high sensitive attributes information,and protection degree is similar on similar sensitivity information.Finally,experiments are carried out on a real data set.The experimental results show that the proposed method can improve the accuracy of recommendation while protecting the user's privacy in the recommendation system.