Promoting accuracy of trust bootstrapping from rating network

doi:10.11772/j.issn.1001-9081.2014.08.2442

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2442-2446.DOI: 10.11772/j.issn.1001-9081.2014.08.2442

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Promoting accuracy of trust bootstrapping from rating network

LIU Bin,ZHANG Renjin

School of Mathematics and Computer Science, Guizhou Normal University, Guiyang Guizhou 550001, China

Received:2014-01-26 Revised:2014-03-09 Online:2014-08-10 Published:2014-08-01
Contact: LIU Bin

从评价网络提升信任启动的准确度

刘彬,张仁津

贵州师范大学数学与计算机科学学院，贵阳550001

通讯作者: 刘彬
作者简介:刘彬(1973-)，男，湖南华容人，副教授，硕士，CCF会员，主要研究方向：可信计算、智能计算；张仁津(1963-)，男，四川成都人，教授，主要研究方向：软件方法学、智能计算。
基金资助:
贵州省科学技术基金资助项目

Abstract

Abstract:

To reduce the influence of evaluating trust of a commodity may be affected easily by unfair and malicious rates when the commodity has only few rates on e-commerce platform, a trust bootstrapping method based on assessing the credibility of the rate was presented. The credibility of a rate was got through evaluating the rates for other commodities and related to the factors of the number of rates by the rater, the rater's transaction amount and the price of the rated commodity. The trust value of a commodity without a rate was derived from the shop to which this commodity belonged and the declared attributes of this commodity. The trust value of a commodity which owned rates with sufficient high credibility was determined by these rates with high credibility. Otherwise the trust value was determined partly by rates or was processed according to a commodity without a rate. Calculation, analysis and experimental results show that this presented method, evaluating the credibility of a rate by its rating network, compared with the conventional method and k-means clustering method, has the smallest error and is not sensitive to the ratio of malicious rates. This method can help users select reliable commodities sold at the initial stage on e-commerce platforms.

摘要：

为了减少电子商务平台中商品只有很少用户评价时评估商品信任值容易受到不公正、恶意评价的影响，提出一种基于评价网络评估评价可信度的信任启动方法。评价的可信度通过评估评价者对其他商品的评价得出，与评价者评价的数量、金额以及被评价商品的价格等因素有关。无评价商品的信任值来源于商品所在的商铺和商品的申明属性。当商品的评价具有足够高的可信度时，它的信任值就由足够可信的评价决定，否则部分由评价决定或按无评价的商品处理。计算、分析和测试结果表明提出的从评价网络评估评价信任度的方法同常规方法、k均值聚类方法相比产生的误差小而且对恶意评价比率不敏感，能有助于用户从电子商务平台选择值得信赖的上架初期的商品。

CLC Number:

TP393.08

LIU Bin ZHANG Renjin. Promoting accuracy of trust bootstrapping from rating network[J]. Journal of Computer Applications, 2014, 34(8): 2442-2446.

刘彬张仁津. 从评价网络提升信任启动的准确度[J]. 计算机应用, 2014, 34(8): 2442-2446.

References

[1]HOFFMAN K, ZAGE D, CRISTINA N R. A survey of attack and defense techniques for reputation systems ［J］. ACM Computing Surveys, 2009, 42(1): 1-31.
[2]NOOR T H, SHENG Q Z, ZEADALLY S, et al. Trust management of services in cloud environment: obstacles and solutions ［J］. ACM Computing Surveys, 2013, 46(1): 271-289.
[3]CALDEIRA F, SCHABERREITER T, MONTEIRO E, et al. Trust based interdependency weighting for on-line risk monitoring in interdependent critical infrastructures ［C］// Proceedings of the 6th International Conference on Risks and Security of Internet and Systems. Piscataway: IEEE, 2011: 1-7.
[4]YOU L J, SIKORA R. An adaptive evaluation mechanism for online traders ［J］. European Journal of Operational Research, 2011, 214(3): 739-748.
[5]KIM Y A, PHALAK R. A trust prediction framework in rating-based experience sharing social networks without a Web of trust ［J］. Information Sciences, 2012, 191(1):128-145.
[6]FOULIRAS P. A novel reputation-based model for e-commerce ［J］. Operational Research, 2014, 13(1): 113-138.
[7]GAN Z, ZENG C, LI K, et al. Construction and optimization of trust network in e-commerce environment ［J］. Chinese Journal of Computers, 2012, 35(1): 27-37.(甘早斌,曾灿,李开,等.电子商务下的信任网络构造与优化［J］.计算机学报,2012,35(1):27-37.)
[8]ZHANG S, FANG Z, CHEN J, et al. Community clustering model for e-commerce trust based on social network ［J］. Journal of Zhejiang University: Engineering Science, 2103, 47(4): 654-661.(张少中,方朝曦,陈军敢,等.基于社会网络的电子商务信任社区聚类模型［J］.浙江大学学报:工学版,2103,47(4):654-661.)
[9]YAHYAOUI H, ZHIOUA S. Bootstrapping trust of Web services through behavior observation ［J］. Knowledge and Information Systems, 2013, 37(2): 389-416.
[10]ALJAZZAF Z, CAPRETZ M A M, PERRY M. Trust bootstrapping services and service providers ［C］// Proceedings of the 9th Annual International Conference on Privacy, Security and Trust. Piscataway: IEEE, 2011: 7-15.
[11]NGUYEN H T, YANG J, ZHAO W L. Bootstrapping trust and reputation for Web services ［C］// Proceedings of the 14th IEEE International Conference on Commerce and Enterprise Computing. Piscataway: IEEE, 2012: 41-48.
[12]MALIK Z, BOUGUETTAYA A. RATEWeb: reputation assessment for trust establishment among Web services ［J］. The VLDB Journal, 2009, 18(4): 885-991.
[13]JIANG L, ZHANG K, XU J, et al. Research on trust transitivity and aggregation in evidential trust model［J］. Journal on Communications, 2011, 32(8):91-100.(蒋黎明,张琨,徐建,等.证据信任模型中的信任传递与聚合研究［J］.通信学报,2011,32(8):91-100.)
[14]TONG X, ZHANG W, LONG Y. Transitivity of Agent subjective trust ［J］. Journal of Software, 2012, 23(11): 2862-2870.(童向荣,张伟,龙宇.Agent主观信任的传递性［J］.软件学报,2012,23(11):2862-2870.)

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Promoting accuracy of trust bootstrapping from rating network

从评价网络提升信任启动的准确度

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