关键词: 隐式信任, 抗攻击, 协同过滤算法, 社会化推荐, 信息安全

Abstract: Under the commercial competition environment, recommendation system is vulnerable to shilling attack. The social recommendation algorithm based on trust relationship is proved to be an effective way to solve the shilling attack problem. However, most of the existing algorithms focus on explicit trust relationships, and implicit trust relationships are not really exploited. A social recommendation algorithm based on multiple implicit trust relation is proposed. Firstly, referring to the framework of trust antecedents in sociology and organizational behavior science, from the two perspectives of global trust and partial trust, we deeply study the extraction and quantification of trust elements. Then, the overall trust degree is obtained by integrating the local trust degree and the global trust degree by the trust adjustment factor. Finally, based on the overall trust of users, the attackers are isolated from trusted neighbors,realization of personalized recommendation. Extensive experiments are carried out on two datasets.The experimental results show that the algorithm improves the recommendation accuracy and effectively suppresses the impact of the shilling attacks.

Key words: implicit trust, anti-attack, collaborative filtering, social recommendation, information security