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Behavior-Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

机译:受限和匿名参与的社交网络中基于行为的信任传播

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Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior-based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as friendship effects. Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship Tell Me Your Friends and I Will Tell You Who You Are, we extend our behavior-based trust model by incorporating the friendship effect with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译:通过金钱和物质激励来增加社交网络中的互动和参与并不总是可行的。一些社交网络,尤其是那些建立在公平基础上的社交网络,不能激励使用有形事物的成员,因此需要无形的方式来这样做。在这样的网络中,个性化的推荐者可以激励成员与社区中的其他成员进行交互。实践证明,基于行为的信任模型通常使用成员与社区中其他成员的交互来计算社会信任值。但是,这些模型在很大程度上忽略了与成员交互的那些成员的交互,这被称为友谊效应。社会研究和行为科学的结果表明,朋友对社区成员的行为有重要影响。遵循著名的西班牙友谊谚语“告诉我你的朋友,我会告诉你你是谁”,我们通过引入友谊效应来扩展基于行为的信任模型,以提高推荐系统的准确性。在本文中,我们描述了一种基于关联的信任传播模型,该模型结合了单个成员及其朋友的行为。在我们的模型中,信任的传播取决于三个关键因素:相互作用的密度,分离的程度以及友谊效应的减弱。我们使用真实的数据集评估模型,并观察有无信任传播的社交网络中发生的情况,以了解信任传播对推荐列表中成员排名的预期影响。我们介绍了模型及其评估结果。这项工作是在主持网络的背景下进行的,只有受邀请才能参与其中,成员是匿名的,并且在社区外彼此不认识。版权所有(c)2014 John Wiley&Sons,Ltd.

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