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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems
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Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems

机译：学习自适应信任力量，具有意识特和受托人的用户角色，以获取信任感知推荐系统

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摘要

There are two key characteristics of users in trust relationships that have been well studied: (1) users trust their friends with different trust strengths and (2) users play multiple roles of trusters and trustees in trust relationships. However, few studies have considered both of these factors. Indeed, it is quite common for someone to respond to his/her friend that they trusted him/her, which indicates that there exist two kinds of information between each pair of users: the trust influence of trustee on truster and the feedback influence of truster on trustee. Considering this problem, we propose a novel adaptive method to learn the trust influence between users with multiple roles of truster and trustee for recommendation. First, we propose to introduce the concept of latent trust strength to learn adaptive role-based trust strength with limited values for each trust relationship between users. Second, because there is only one training example to learn each parameter of latent trust strength, we further propose two regularization methods by building relations between latent trust strength and user preferences to guide the training process of latent trust strength. After that, we develop a new recommendation method, RoleTS, by integrating the role-based trust strength into a previous recommendation model, TrustSVD, which considers both explicit and implicit information of trust and ratings. We also conduct a series of experiments to study the performance of the proposed method. Experimental results on two public real datasets demonstrate that the proposed method performs better than several state-of-the-art algorithms.
机译：有两个人在学习的信任关系中有两个关键特征：（1）用户信任他们的朋友不同的信任优势和（2）用户在信任关系中发挥信任和受托人的多个角色。但是，很少有研究考虑过这两种因素。事实上，有人对他/她的朋友回应他们信任他/她是非常常见的，这表明每对用户之间存在两种信息：受托人对意想象的信任影响以及意味着反馈影响在受托人。考虑到这个问题，我们提出了一种新颖的自适应方法，以了解具有意识解和受托人的多种角色的用户之间的信任影响。首先，我们建议介绍潜在信任力量的概念，以了解基于适应性的角色的信任力量，为用户之间的每个信任关系有限。其次，由于只有一个训练示例来学习每个参数的潜在信任实力，我们进一步提出了两个正规化方法，通过建立潜在信任力量和用户偏好之间的关系来指导潜在信任力量的培训过程。之后，我们通过将基于角色的信任实力集成到先前的建议书籍，TrustSVD，通过将基于角色的信任实力集成到了一个新的建议书，这是一项新的推荐方法，这是一项顾客和评级的明确和隐含信息。我们还开展了一系列实验来研究所提出的方法的性能。两个公共实时数据集上的实验结果表明，所提出的方法比几种最先进的算法表现更好。

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