Research on Trust Management Model for E-Commerce Based on Fuzzy Clustering Method

Piao, Chunhui; Gan, Xiangchao

doi:10.1109/icebe.2009.33

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…Typically trust models can be classified into C2C, B2C and B2B e-commerce trust models, respectively. Reputation-based trust models adopted by eBay, Amazon and Taobao are the most famous C2C ecommerce trust models [3,4,5]. Studies on B2C e-commerce trust models mainly focus on the trust mechanism of ecommerce platform, determinants of trust of customers in ecommerce and strategies contributing to improving customers trust [6,7,8].…”

Section: B E-commerce Trust Model and M-commerce Trust Modelmentioning

confidence: 99%

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Mobile Commerce Trust Model and its Application for Third Party Trust Service Platform

Piao

Wang

Wen

et al. 2012

2012 IEEE 14th International Conference on Commerce and Enterprise Computing

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A theory framework of influence factors of consumers' trust in mobile commerce is established. Then a questionnaire on trust evaluation of mobile commerce is designed, and reliability analysis and validity analysis on the trust evaluation scale are conducted respectively. Based on SEM, first-order confirmatory factor analysis is used to test the established trust evaluation model for mobile commerce; and second-order factor analysis is applied to determine the weights of trust influence factors. An indicator system for evaluating the trustworthiness of mobile vendors is proposed. The openness for the transaction-related data of four main mobile commerce platforms is analyzed. An ELECTRE -based algorithm for evaluating the trustworthiness of mobile-vendors is designed. Empirical analysis shows that the proposed trust model and trustworthiness evaluation algorithm can be rationally and meaningfully used to make mobile vendors recommendation by third party trust service provider.

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Section: B E-commerce Trust Model and M-commerce Trust Modelmentioning

confidence: 99%

“…The items were measured on a five-point Likert scale, ranging from "strongly disagree" (1) through "neutral" (3) to "strongly agree" (5). The measurement scale of m-commerce trust is as shown in Table 1.…”

Section: A Questionnaire Design and Data Collectionmentioning

confidence: 99%

Mobile Commerce Trust Model and its Application for Third Party Trust Service Platform

Piao

Wang

Wen

et al. 2012

2012 IEEE 14th International Conference on Commerce and Enterprise Computing

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“…The direct trust of a recommender (buyer) in a target (seller) can be represented by transaction-related data between them simply--such as the number of transactions, feedback ratings of buyer to seller, transaction amount and so on, and can be computed through kinds of trust evaluation models [3,18,19]. For example, based on the following four trust attributes related to transactions: The average amount of per transaction;…”

Section: ) the Direct Trust Of Recommender In Targetmentioning

confidence: 99%

“…The analytic hierarchy process (AHP) is adopted to calculate the weight coefficient of trust attributes, and then compute the direct trust of a recommender in a target through the weighted sum method [18].…”

Section: ) the Direct Trust Of Recommender In Targetmentioning

confidence: 99%

Research on Web-of-Trust-Based Personalized Seller Recommendation Algorithm for E-Commerce

Piao

Wang

Pan

et al. 2011

2011 IEEE 8th International Conference on E-Business Engineering

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Trust evaluation and personalized recommendation are two challenge problems in large scale e-commerce applications. In this paper, we present a novel web-of-trustbased personalized seller recommendation algorithm. Starting from transaction-related data and trading relationship, the buyer-buyer web-of-trust and buyer-seller web-of-trust for recommendation and related concepts are formally defined. The method of constructing the web-of-trust is discussed. The algorithms computing direct trust and recommending trust between buyers based on web-of-trust are presented, and the algorithm for computing direct trust of the buyer in the seller is described. Aiming at the needs of the user intending to buy a specific product, the web-of-trust-based personalized seller recommendation algorithm is proposed, which can be used to find the most trustworthy seller selling the specified product for the buyer. The algorithms for computing direct trust of the recommender in target seller and comprehensive trust of the requester in target seller are designed. Using an example, the process of generating seller recommendation is described, and the recommendation results are analyzed.

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“…The reputation system that is built on e-commerce application mostly very simple, where the system is going to assess the reputation of a seller based on feedback or satisfaction score given by the buyer after the buyer transact with the seller [1]. Then, the reputation score of a seller is the average of the value of the buyer's satisfaction that he get [9]. So, the reputation system relies on feedback rating that is obtained from system users.…”

Section: A Simple Model Of Reputation Systemmentioning

confidence: 99%

Implementation of seller-buyer closeness degree-based reputation system for e-commerce

Dharmaadi

Suhardi

Hidayat

et al. 2014

2014 International Conference on Information Technology Systems and Innovation (ICITSI)

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Past 5 years, the number of internet users were increased rapidly so that e-commerce market was grew fast too. Nonetheless, e-commerce application has a crucial problem, that is the lack of information about sellers behavior. Buyers often feel hesitate to make transaction at e-commerce site because they are afraid of being cheated by the seller. Our previous paper has proposed a new reputation system based on seller-buyer closeness degree for e-commerce. Now, in this paper, we develop implementation algorithm of our previous reputation system and discuss about its usage. This algorithm is helpful for e-commerce developers that want to easily implement our reputation system in their C2C e-commerce. Consequently, the proposed reputation system can be understood and implemented easily and quickly in real e-commerce industry.

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Research on Trust Management Model for E-Commerce Based on Fuzzy Clustering Method

Cited by 5 publications

References 3 publications

Mobile Commerce Trust Model and its Application for Third Party Trust Service Platform

Mobile Commerce Trust Model and its Application for Third Party Trust Service Platform

Research on Web-of-Trust-Based Personalized Seller Recommendation Algorithm for E-Commerce

Implementation of seller-buyer closeness degree-based reputation system for e-commerce

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