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.
In C2C e-commerce systems, transactions are anonymous, random and dynamic. Since the transaction information is exchanged between the partners by the virtual network, the partners lack the basic trust foundations and there exist high risks in the process of the transactions. One of the efficient ways to reduce the transaction risk is to evaluate the seller's trustworthiness and help the buyer make scientific decision by trust models. From the buyer's perspective, this paper presents a C2C dynamic trust algorithm (CDTA) for the e-commerce environment. The algorithm takes into account the attributes of trust and trust network, such as the time sensitiveness, the asymmetry of the trust, and the transitivity and selectivity of the trust propagation paths. First, the direct trustworthiness of the buyer to the seller is computed by the transaction experience between them. Second, the reference trustworthiness is computed from the buyer's friends in the trust network according to the recommendation confidence. Finally, the trust of the buyer to the seller is acquired through the integration of the direct trustworthiness and the reference trustworthiness with the trust adjusting factor. The experiments show that the granularity of the trust evaluation is more fine-grained and the evaluation result is more objective than existing work. On the other hand, the similarity review can help the buyer sift out the reference nodes meeting with the buyer's preference, make the reference trustworthiness more credible, and resist the attacks from malicious nodes.