基于信任网络的C2C电子商务信任算法

C2C电子商务交易具有匿名性、随机性、动态性的特点,交易双方仅通过虚拟网络交换信息,缺乏基本的信任基础,交易存在较大的风险.构造科学的信任计算模型、客观度量卖家的可信度、辅助买家(消费者)做出正确的购买决策,是降低交易风险的有效手段之一.为此,从买家的角度出发,详细讨论了信任网络的基本概念及其相关属性,并以信任的时间敏感性、不对称性、可传递性和可选择性为基础,建立了C2C电子商务环境下的动态信任算法(C2C dynamic trust algorithm,简称CDTA).该算法首先通过买家自身的交易经验计算买家对卖家的直接信任度,然后计算来自信任网络中买家的朋友对卖家的推荐信任度,最后通过信任调节因子集成直接信任度和推荐信任度来获得买家对卖家的信任度.仿真实验分析结果表明:一方面,该算法考虑了交易的多属性及其相关性,信任评价的粒度更加细化,使得信任计算的结果更加客观;另一方面,评价相似度可以很好地筛选出符合买家“个性”的推荐节点,使推荐信任度更准确,可以进一步抑制恶意节点对信任算法的影响.     

C2C信用评价体系的风险计算

目前,C2C电子商务网站纷纷建立了较合理的评价体系,但在交易中仍然存在风险问题。本文通过对C2C模式交易过程的分析,提出了一种根据历史交易的情况,综合考虑卖家的信用值、买家的信用等级以及商品价格等因素的信用风险计算方法。通过模拟实验表明,对目前网上购物的信用炒作起到了一定的防御作用,有助于交易者做出正确的交易决策,为电子商务网站提供了更有效的信用评价信息。     

基于社会网络与信誉的C2C信任评价模型

将概率论和社会网络理论应用到信任传递以及多信任路径建模中,针对C2C交易平台实际的商业网络特征,提出基于社会网络与信誉的C2C信任评价模型。引入相似度计算建立簇与簇之间的关联,以解决C2C交易网络存在的买卖分工明确、稀疏性、聚簇问题。对淘宝网C2C交易平台数据的分析表明,该模型对淘宝网现有信誉机制具有较好的改进和补充作用,更符合现实生活中人与人之间的信任和推荐关系。     

基于三维协同过滤的C2C电子商务推荐系统

个人对个人电子商务(customer to customer,C2C)是目前主流的电子商务模式之一,为解决C2C电子商务网站中特殊的推荐问题,对传统的二维协同过滤方法进行了扩展,提出了能进行卖家和商品组合推荐的三维协同过滤推荐方法,并在此基础上设计了C2C电子商务推荐系统,阐述了该系统的基本架构和推荐过程中的关键运算.该系统利用卖家属性计算卖家相似度,并依据销售关系和卖家相似度对评分数据集进行填充,以解决三维评分数据的稀疏问题;采用协同过滤思想,利用历史评分计算买家相似度,获取最近邻并预测未知评分,最终将预测评分最高的卖家和商品组合推荐给目标买家.实验结果表明,该系统具有较好的推荐效果.     

易趣:无所谓B或C

从一家C2C个人拍卖竞价网站向个人电子商务网站过渡。易趣CEO、大头神童邵亦波表示,“易趣始终坚持为个人用户提供交易服务,帮助买家找到卖家、找到价廉物美的物品,帮助卖家找到合适的买家。这里的买家与卖家是个人或是商家,并没有一定之规,不同的情况下买家与卖家也没有一定的界限。我们提供的是一样的服务。”易趣的目标非常明确:一心一意,直指赢利。     

C2C电子商务信任评价模型设计与实现

目前C2C电子商务环境下使用的信任评价模型存在诸多问题,提出基于交易成功率的C2C电子商务信任评价模型,并从模型框架、信任值计算、信任评价模型算法、用户信任值存储、仿真实验等多个方面对模型进行阐述。通过仿真实验,从评价准确率和交易成功率两点就本模型与淘宝的信任评价模型进行比较。     

基于多因素的信任计算模型研究

电子商务市场是一个信息不对称的市场,迫切需要对该环境中参与者的可信度和服务能力进行判断。信任和信誉管理为交易双方提供了一个互相了解的平台,成为研究焦点。研究了电子商务中的信任问题,探讨电子商务的信任和信誉概念,结合当前信任和信誉计算不足,提出了一种基于多因素用户信任计算模型。     

基于神经网络和SNA的C2C电子商务信誉欺诈识别研究*

当前,互联网中信誉欺诈行为严重影响了C2C电子商务的发展。如何有效识别互联网上的信誉欺诈商户是当前的研究热点,文中阐述了将基于交易历史的社会网络分析用于构建C2C电子商务信誉欺诈识别指标体系的原理及过程,通过使用LVQ神经网络对雅虎奇摩拍卖网采集的大量用户交易数据进行分类,有效识别出控制多个虚假账户以达到信用累计目的的信誉欺诈商户,对维护C2C电子商务交易的稳定性具有重要意义。     

一种抗欺诈的C2C卖方信誉计算模型研究

针对C2C信誉模型中小额商品信誉炒作、信誉共谋、信誉诋毁等问题,引入交易价格、反馈可信度、共谋因子等参数,提出一种买方视角下抗欺诈的卖方成员信誉计算模型(C2CRep)。实验中通过收集网络交易数据,定义可疑欺诈的基本特征对数据进行抽取,并设定信誉计算误差(RCE)指标检验由欺诈行为带来的信誉值在社区信誉所占比例来检验模型的应用效果。结果表明,C2CRep在3类不同比例的欺诈行为中,RCE明显低于SPORAS与淘宝信誉模型,且RCE值在3类实验中都低于15%,抗欺诈性强。     

基于云模型的C2C电子商务信任评价模型

针对C2C电子商务存在的交易安全性问题,提出了基于云模型理论的信任评价模型。通过信任云及信任标准云的定义,客观地反映了信任的模糊性和随机性,实现了信任从定性到定量间的相互转换。给出了信任云的合并及相似度计算算法,实现了信任的分属性及综合评价和决策,仿真实验表明模型是可行的和有效的。     

Reputation mechanism for e-commerce in virtual reality environments

The interest in 3D technology and virtual reality (VR) is growing both from academia and industry, promoting the quick development of virtual marketplaces (VMs) (i.e. e-commerce systems in VR environments). VMs have inherited trust problems, e.g. sellers may advertise a perfect deal but doesn’t deliver the promised service or product at the end. In view of this, we propose a five-sense feedback oriented reputation mechanism (supported by 3D technology and VR) particularly for VMs. The user study confirms that users prefer VMs with our reputation mechanism over those with traditional ones. In our reputation mechanism, five-sense feedback is objective and buyers can use it directly in their reputation evaluation of target sellers. However, for the scenarios where buyers only provide subjective ratings, we apply the approach of subjectivity alignment for reputation computation (SARC), where ratings provided by one buyer can then be aligned (converted) for another buyer according to the two buyers’ subjectivity. Evaluation results indicate that SARC can more accurately model sellers’ reputation than the state-of-the-art approaches.     

Trust in consumer-to-consumer electronic commerce

We developed a model of consumer-to-consumer (C2C) e-commerce trust and tested it. We expected that two influences: internal (natural propensity to trust [NPT] and perception of web site quality [PWSQ]) and external (other's trust of buyers/sellers [OTBS] and third party recognition [TPR]) would affect an individual's trust in C2C e-commerce. However contrary to studies of other types of e-commerce, support was only found for PWSQ and TPR; we therefore discussed possible reasons for this contradiction. Suggestions are made of ways to help e-commerce site developers provide a trustworthy atmosphere and identify trustworthy consumers.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention

In China, major C2C websites are focusing on increasing their customer bases by converting members of their virtual communities (VCs) into C2C buyers and sellers. This phenomenon is called e-commerce based on social networks (ENS). The current research analyzes what factors affect trust building among VC members and how this trust influences the trust in the C2C website or vendor. We propose and empirically test a model of trust in VCs based on the trust formation mechanism. Using data collected from Taobao Virtual Community, we show that familiarity, perceived similarity, structural assurance, and trust propensity are important antecedents to trust in members in VCs. Analyses of the two kinds of trust show that trust in members’ ability significantly affects three dimensions of trust in the vendor/website in terms of ability, integrity, and benevolence. In addition, trust in members’ integrity and benevolence stimulates the purchase intention and trust in the vendor/website’s ability positively affects the intention to get information and the purchase intention.     

Evaluating transaction trust and risk levels in peer-to-peer e-commerce environments

Trust evaluation is critical to peer-to-peer (P2P) e-commerce environments. Traditionally the evaluation process is based on other peers' recommendations neglecting transaction amounts. This may lead to the bias in transaction trust evaluation and risk the new transaction. The weakness may be exploited by dishonest sellers to obtain good transaction reputation by selling cheap goods and then cheat buyers by selling expensive goods. In this paper we present a novel model for transaction trust evaluation, which differentiates transaction amounts when computing trust values. The trust evaluation is dependent on transaction history, the amounts of old transactions, and the amount of the new transaction. Therefore, the trust value can be taken as the risk indication of the forthcoming transaction and is valuable for the decision-making of buyers.     

Antecedents of trust in online auctions

Customer loyalty or repeat purchasing is critical to the online auction sellers’ survival and success. Previous research has established that online repeat purchase intentions are the product of buyer assessments of trust in the online seller. Previous research has also affirmed the importance of justice perceptions in engendering trust. These perspectives, however, have been examined independently by IS and management researchers. By integrating these two perspectives, a richer understanding of buyers’ underlying beliefs and subsequent repeat purchase intentions can be gained. In the research model, bidding justice is proposed as a formative second-order construct driven by distributive justice, procedural justice, interpersonal justice, and informational justice. Bidding justice is hypothesized to positively affect trust in the community of sellers, which in turn is hypothesized to positively affect repeat purchase intentions. Data collected from 412 buyers in Yahoo-Kimo’s online auction marketplace provide support for the proposed model. The study shows that trust is a significant positive predictor of buyers’ intentions to repeat purchase. The study also shows that the four dimensions of justice are important components of bidding justice, which in turn has a strong positive effect on trust in the community of sellers. Implications for theory and practice and suggestions for future research are discussed.     

On Desideratum for B2C E-Commerce Reputation Systems

This paper reviews existing approaches to reputation systems, their constraints as well as available solutions. Furthermore, it presents and evaluates a novel and comprehensive reputation model devoted to the distributed reputation system for Business-to-Consumer (B2C) E-commerce applications that overcomes the discussed drawbacks. The algorithm offers a comprehensive approach as it considers a number of issues that have a bearing on trust and reputation such as age of ratings, transaction value, credibility of referees, number of malicious incidents, collusion and unfair ratings. Moreover, it also extends the existing frameworks based on information about past behaviour, with other aspects affecting online trading decisions which relate to the characteristic of the providers, such as existence of trustmark seals, payment intermediaries, privacy statements, security/privacy strategies, purchase protection/insurance, alternative dispute resolutions as well as the existence of first party information.     

The agent-based negotiation process for B2C e-commerce

B2C e-commerce is becoming more widespread as more people come to recognize its convenience and its ability to rapidly respond to requests and as more products and services become available. However, many electronic marketplaces, especially in the business-to-consumer, are in essence some kind of search engine where buyers look for the best product in a database of products offered by sellers. Usually, such e-marketplaces do not use agent technology at all although agents could significantly improve the services provided both for the buyers and the sellers. Further, negotiation capabilities are essential for B2C e-commerce systems. In an automated negotiation, intelligent agents engage in broadly similar processes to achieve the same end. In more detail, the agents prepare bids for and evaluate offers on behalf of the parties they represent with the aim of obtaining the maximum benefit for their users. Nevertheless, in the current situation, price is the only criterion by which agents are created. This factor is easy to measure and automate. However, the criteria for advanced transactions need to be elaborated, for example, details of giveback and dividend. In this paper, we present a multiple-attributes negotiation model for B2C e-commerce, which deploys intelligent agents to facilitate autonomous and automatic on-line buying and selling by intelligent agents while quickly responding to consumers. These include a 4-phase model, information collection, search, negotiation, and evaluation. We also apply fuzzy theory and analytical hierarchy process to develop the system interface to facilitate the user inputs. Finally, an example of the notebook purchasing process is illustrated.     

基于信誉推荐的网络购物主观信誉模型分析

针对网络购物中买卖双方的信任问题,建立了基于信誉推荐的主观信誉模型。该模型不仅引入了交易时间、交易金额及信誉推荐可信度等客观因素,还引入了第一印象等主观因素。基于Matlab的仿真实验表明,[Zi→j]模型能保证交易成功率的成功率在0.8以上,而且[Zi→j]模型比其他的信誉模型能更好地对抗恶意节点（特别是间歇性恶意节点）的恶意行为,具有较高的稳定性。     

C2C商家信任度动态分类机制研究

在C2C电子商务的交易中,买家因交易目的不同,对商家信任度的粒度要求是不同的。为提高交易的成功率,应建立商家信任度的动态分类机制,以满足不同应用的需求。为此,从买家对商家信任的本质出发,首先通过使用模糊集合理论的隶属度概念,提出了信任的定量描述方法;其次,引进模糊聚类分析方法,建立了商家信任度的动态分类机制;最后,通过实验仿真分析,验证了该方法的有效性和合理性。