A semantic and social-based collaborative recommendation of friends in social networks

The development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user-based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social-behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user-based CF algorithm and solves the sparsity and cold start problems.     

基于用户信任和张量分解的社会网络推荐

社会化网络中的推荐系统可以在浩瀚的数据海洋中给用户推荐相关的信息。社会网络中用户之间的信任关系已经被用于推荐算法中,但是目前的基于信任的推荐算法都是单一的信任模型。提出了一种基于主题的张量分解的用户信任推荐算法,用来挖掘用户在不同的物品选取的时候对不同朋友的信任程度。由于社交网络更新速度快,鉴于目前的基于信任算法大都是静态算法,提出了一种增量更新的张量分解算法用于用户信任的推荐算法。实验结果表明：所提出的基于主题的用户信任推荐算法比现有算法具有更好的准确性,并且增量更新的推荐算法可以大幅度提高推荐算法在训练数据增加后的模型训练效率,适合更新速度快的社会化网络中的推荐任务。     

Strategies for predicting local trust based on trust propagation in social networks

Trust plays a critical role in determining social interactions in both online and offline networks, and reduces information overload, uncertainties and risk from unreliable users. In a social network, even if two users are not directly connected, one user can still trust the other user if there exists at least one path between the two users through friendship networks. This is the result of trust propagation based on the transitivity property of trust, which is “A trusts B and B trusts C, so A will trust C”. It is important to provide a trust inference model to find reliable trust paths from a source user to an unknown target user, and to systematically combine multiple trust paths leading to a target user. We propose strategies for estimating level of trust based on Reinforcement Learning, which is particularly well suited to predict a long-term goal (i.e. indirect trust value on long-distance user) with short-term reward (i.e. direct trust value between directly connected users). In other words, we compare and evaluate how the length of available trust paths and aggregation methods affects prediction accuracy and then propose the best strategy to maximize the prediction accuracy.     

Tele‐Cocooning: Mobile Texting and Social Scope

The present study examines the tele‐cocooning hypothesis in the context of general trust using a nationally representative survey of Japanese youth. We find that although frequency of texting is positively correlated with general trust, this correlation is spuriously caused by how heavy mobile texters interpret the words “most people” in the general trust measurement. Heavy users assume that “most people” only refers to friends, family, and others going to the same school. When the effect of the “most people” assumption is controlled, the positive association between texting and general trust disappears. Further exploration of the data shows that heavy texting nevertheless has negative implications for social tolerance and social caution, both of which are theoretically proximate to general trust.     

基于社会关系的互联网信任建立模式研究

本文从社会关系的角度研究了互联网信任的建立模式,主要对基于信任传递和基于信任评估的信任建立两种模式进行了研究和阐述,并以人人网和淘宝网为例进行了信任建立模式的分析。对人人网中好友数量及无效连接过多引发的信息爆炸问题,采用好友分类和划分关系连接权重的方法改进信任建立过程,而对淘宝网的信用评价体系采用基于物品、服务、成交金额加权计分的方法进行优化。     

一种基于社会网络的信任模型

社会网络是通过朋友关系、工作关系和信息交换等一组社会机制,将人、组织和其他社会团结联系在一起形成的网络,Web就是一种社会网络.在社会网络中要求有一种自然的机制对网络的真实性进行判断和评价,信任模型就是比较好的一种机制.基于社会网络,本文构建了一种网络信任模型,用来估算信任者和被信任者之间的信任等级,能够用于语义网的信任管理系统和社会网络的信任评价中.实验结果表明,该模型具有很好的效率和效果.     

利用信任支持度构建客户信任网络

利用信任的社会性质进行信任传递,可有效缓解数据稀疏的问题,提高推荐系统的覆盖率和准确率。目前对信任网络的研究存在信任模型建立不准确、信任传递机制复杂与失真等问题。为准确表述信任网络中的客户信任关系,引入信任支持度的概念,提出了一种信任度与信任支持度相结合的客户信任模型;制定了符合信任社会性的传递规则,构建了基于该模型的客户信任网络,并设计了相应的个性化推荐算法。实验结果表明,此模型提高了推荐系统的覆盖率、准确率及推荐质量。     

The influence of familiarity,trust and norms of reciprocity on an experienced sense of community: an empirical analysis based on social online services

The growing use of social online services raises the question of what encourages members to participate actively and maintain accumulated social capital. Our research has particularly become aware of the relevance of familiarity, trust and reciprocity in understanding the members' sense of a virtual community (VC). Familiarity and trust are efficient criteria to assess and determine the extent to which one should engage in a virtual relationship. Furthermore, the effort of sharing experiences and knowledge must be based on the expectation of receiving certain returns. A structural equation modelling, specifically partial least squares, is proposed to assess the relationships between the constructs. Overall, the empirical results provided strong support for the hypotheses. Familiarity and trust lead the member to develop a growing perceived community support (PCS) and significantly influence the sense of a VC. Norms of reciprocity directly influence affective trust and PCS. Higher familiarity does not moderate the impact of affective trust on PCS, however. The results of this study could thus help social online service providers to create a successful business model and to determine the main drivers of the members' sense of a VC.     

结合评分和信任关系的社会化推荐算法

针对推荐系统中普遍存在的数据稀疏和冷启动等问题,提出一种综合评分和信任关系的社会化推荐算法。首先对网络中新用户的初始信任值进行合理赋值,有效地解决了新用户的信任冷启动问题。鉴于用户的喜好会受其朋友的影响,推荐模型又利用朋友之间的信任矩阵对用户自身的特征向量进行修正,解决了用户特征向量的精准构建及信任传递问题。实验结果表明,所提算法较传统的社会网络推荐算法在性能上有显著提高。     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

基于图神经网络的异构信任推荐算法

传统基于图神经网络的社交推荐算法通过加强用户和项目特征的学习提升预测精度,但随着用户数据日益稀疏和社交关系趋于复杂,推荐质量提升缓慢。为挖掘用户和项目的潜在关联关系,提出一种结合图神经网络的异构信任推荐算法（GraphTrust）。在显式信任关系的基础上获取用户的潜在好友,根据动态影响力传播模型将图神经网络中的节点和边进行分类,通过不同类型的边在不同节点间进行影响力传播扩散,捕捉隐藏在高阶网络结构中的影响力扩散特征,并使用户和项目的潜在特征随着影响力传播过程达到平衡状态,最终将用户交互的项目特征作为辅助特征与用户特征聚合进行评分预测。在Yelp和Flickr数据集上的实验结果表明,当潜在特征维数为64时,GraphTrust算法相比于DiffNet++算法的命中率和归一化折损累计增益分别提升了13.2%、22.2%和20.4%、25.5%,在一定程度上提高了推荐过程的可解释性和预测精度,并且缓解了数据稀疏问题。     

Mining Trust Relationships from Online Social Networks

With the growing popularity of online social network,trust plays a more and more important role in connecting people to each other.We rely on our personal trust to accept recommendations,to make purchase decisions and to select transaction partners in the online community.Therefore,how to obtain trust relationships through mining online social networks becomes an important research topic.There are several shortcomings of existing trust mining methods.First,trust is category-dependent.However,most of the methods overlook the category attribute of trust relationships,which leads to low accuracy in trust calculation.Second,since the data in online social networks cannot be understood and processed by machines directly,traditional mining methods require much human effort and are not easily applied to other applications.To solve the above problems,we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically.We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships.We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort.We perform experiments on real-life data extracted from Epinions.The experimental results verify the effectiveness and wide application use of our proposed method.     

Understanding civic engagement behaviour on Facebook from a social capital theory perspective

This paper applies the social capital theory to construct a model for investigating the factors that influence online civic engagement behaviour on Facebook. While there is promising evidence that people are making concerted efforts to adopt Facebook to address social issues, research on their civic behaviour from a social capital viewpoint in the social media context remains limited. This study introduces new insights into how Facebook is shaping the landscape of civic engagement by examining three dimensions of social capital – social interaction ties (structural), trust (relational), and shared languages and vision (cognitive). The study contends that these dimensions will influence individuals’ online civic engagement behaviour on Facebook. We also argue that social interaction ties can engender trust, and shared languages and vision among its members, and that shared languages and vision can increase trust among Facebook members. Empirical data collected from 1233 Facebook users provide support for the proposed model. The results help in identifying the motivation underlying the online civic engagement behaviour of individuals in a public virtual community. The implications for theory and practice and future research directions are discussed.     

Examination of community identification and interpersonal trust on continuous use intention: Evidence from experienced online community members

Members’ continued intention to use a community service is rooted in social identity. Past research has examined social identity but ignored the development of interpersonal trust through community identification. Drawing on social identity theory, one can conclude that community identification is the foundation of a member’s continuous use intention through interpersonal trust. Data come from responses to a two-stage survey of experienced members of an online game-based community. The findings support the belief that the identity of the community will strengthen long-term relationships within it. The projection of community identification on continuous use intention is tied to interpersonal trust.     

基于社会学理论的信任关系预测模型

随着社会网络的盛行,信任作为用户之间交互的基础,在信息共享、经验交流和社会舆论方面发挥着重要作用。然而,信任是一个复杂而抽象的概念,受多种因素影响,很难识别信任形成的诱因以及其形成机制。由于来自社会科学的社会学理论有助于解释社会现象,而社会网络反映了现实世界中用户之间的联系,因此,从社会学角度出发,通过研究社会等级理论和同质性理论获取信任关系的发展规律,进而构建信任关系预测模型。首先,对社会等级理论和同质性理论进行阐述,并验证了社会等级理论和同质性理论在社会网络中的存在;然后,分别针对社会等级理论和同质性理论对信任关系的影响提出社会等级正则化方法和同质性正则化方法;最后,利用非负矩阵的三维分解方法并结合社会等级理论和同质性理论实现对信任关系预测的建模,并提出 SocialTrust 模型用于信任关系预测。实验结果表明,相比于其他方法,该方法在信任关系预测方面具有较高的精度。     

Using Estimated Personality of Social Network Members for Finding Influential Nodes in Viral Marketing

Sending promotional messages to a few numbers of users in a social network can propagate a product through word of mouth. However, choosing users that receive promotional messages, in order to maximize propagation, is a considerable issue. These recipients are named “influential nodes.” To recognize influential nodes, according to the literature, criteria such as the relationships of network members or information shared by each member on a social network have been used. One of the effective factors in diffusion of messages is the personality characteristics of members. As far as we know, although this issue is considerable, so far it has not been applied in the previous studies. In this article, using the graph structure of social networks, two personality characteristics, openness and extroversion, are estimated for network members. Next, these two estimated characteristics together with other characteristics of social networks, are considered as the criteria of choosing influential nodes. To implement this process, the real coded genetic algorithm is used. The proposed method has been evaluated on a dataset including 1000 members of Twitter. Our results indicate that using the proposed method, compared with simple heuristic methods, can improve performance up to 37%.     

Who to Trust? Applying Trust to Social Commerce

Organizations have recognized the revenue potential for social commerce (purchasing products via social networks), but such transactions only comprise a small percentage of revenue. Companies have yet to determine the factors that contribute to social commerce failure. In social networking, trust is established when two parties have a history of trustworthy interactions. Acknowledging that social commerce represents a fundamentally different purchasing environment than typical business transactions, we examine the role of trust in determining consumers’ decisions to engage in social commerce for their purchases. We apply trust transfer theory to the social commerce context to assess whether trust in known entities can be transferred to business transactions facilitated through a social network with unknown parties. Using a field survey, we found that trust in the Internet and trust in firms significantly influence consumers’ trust and ultimately their intention to engage in social commerce. Implications for research and practice are discussed.     

社会劳动保障机构网络信任评价体系的研究

根据我国劳动保障公共服务业务代理机构信任的现状和存在的问题,提出了对社会劳动保障机构网络信任评价体系进行研究,构建了网络信任评价体系模型,并对其中的身份信任技术和行为信任技术进行了深入研究,最后对信任评价体系的接口进行了设计,为构建我国劳动保障公共服务机构网络信任评价的可信支撑环境提供了技术支撑.     

移动社会网络中基于全局信任模型的用户影响力计算

针对现有算法和模型对于网络中用户影响力计算大多只考虑拓扑结构和贪心算法而较少考虑真实社会网络中信任度对于节点影响力的重要性这一问题, 该文提出一种全局信任模型(global trust model, GTM)用于评估节点的影响力. 首先计算节点与邻居节点间的信任关系作为局部信任度, 其次利用Beta信誉模型在节点局部信...     

基于信任评估的信息形式化推荐方法

针对网络信息推荐中缺乏信任评估机制的问题,提出一种基于信任评估的信息形式化推荐方法。建立信息推荐的形式化模型,根据信息推荐中推荐源、推荐者和接收者等不同身份的节点,给出节点置信度、节点信任关系等信任评估方法,综合计算得到信息推荐路径的可信任度,在此基础上给出基于信任的信息推荐算法。实验结果证明了该方法的有效性。