Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

先令攻击一直是协作过滤（CF）推荐器系统的重要漏洞，并且对CF推荐器算法的信任已被证明有助于提高系统推荐的准确性。由于已经有一些研究致力于信任这一领域，因此我们探索了使用信任来抵御先令攻击的好处。我们提出的流派信任度不是简单地使用用户生成的信任值，而是根据项目的流派而有所不同，并同时考虑了信任值和用户信誉。本文介绍了各种类型的先令攻击方法，以试图研究用户的信任值和行为特征对防御先令攻击的影响。同时，它改进了用于计算用户相似度以基于体裁信任度形成推荐模型的方法。在Ciao数据集上评估了基于体裁信任的推荐系统的性能。实验结果表明，推荐用于防御不同类型的先令攻击的较高且可比的体裁信任度。