Yan Wang

Publications (67) View all

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  Available from: Yan Wang

  Dataset: Joe-AST ICWS2010 final[1]

  Joe Zou, Yan Wang, Kwei-Jay Lin
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  Available from: Yan Wang

  Dataset: Joe contract SCC2010 final[1]

  Joe Zou, Yan Wang, Kwei-Jay Lin
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  Available from: Yan Wang

  Dataset: ecweb-12 final[1]

  Yan Wang, Vijay Varadharajan
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  Available from: Yan Wang

  Conference Proceeding: A Trust Vector Approach to Transaction Context-Aware Trust Evaluation in E-commerce and E-service Environments

  Haibin Zhang, Yan Wang, Xiuzhen Zhang
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  ABSTRACT: At some e-commerce websites (such as eBay), a trust value of a seller is computed based on the ratings of past transactions given by buyers, which can only reflect the general or global trust level of a seller without any transaction context information taken into account. As a result, a buyer may be easily deceived by a malicious seller in a forthcoming transaction. For example, with the notorious value imbalance problem, a malicious seller can build up a high trust level by selling cheap products and then start to deceive buyers in selling expensive products. In this paper, we first model all contextual transaction factors that reflect the nature of transactions, and thus influence the evaluation of transaction trust. In addition, instead of providing a single trust value, we propose a trust vector approach that takes into account the contextual factors in transactions. Our model systematically categorize these factors into service aspect and transaction aspect. In par-ticular, the computation of the elements in this trust vector is associated with both the context of past transactions and the context of a forthcoming transaction, so as to comprehensively indicate the trust level of a seller for the forthcoming transaction. The computed trust vector can be taken as the reputation profile of the seller. Empirical studies illustrate that it is important and necessary to introduce contextual transaction factors in evaluating the trust level of sellers objectively.
  The 2012 5th IEEE International Conference on Service Oriented Computing & Applications (SOCA 2012); 12/2012
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  Available from: Yan Wang

  Article: The Study of Trust Vector Based Trust Rating Aggregation in Service-Oriented Environments

  Lei Li, Yan Wang
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  ABSTRACT: In most existing studies on trust evaluation, a single trust value is aggregated from the ratings given to previous services of a service provider, to indicate his/her current trust level. Such a mechanism is useful but may not be able to depict the trust features of a service provider well under certain circumstances. Alternatively, a complete set of trust ratings can be transferred to a service client for local trust evaluation. However, this incurs a big overhead in communication, since the rating dataset is usually in large scale covering a long service history. The third option is to generate a small set of data that should represent well the large set of trust ratings of a long time period. In the literature, a trust vector approach has been proposed, with which a trust vector of three values resulting from a computed regression line can represent a set of ratings distributed within a time interval (e.g., a week or a month, etc.). However, the computed trust vector can represent the set of ratings well only if these ratings imply consistent trust trend changes and are all very close to the obtained regression line. In a more general case with trust ratings for a long service history, multiple time intervals have to be determined, within each of which a trust vector can be obtained and can represent well all the corresponding ratings. Hence, a small set of data can represent well a large set of trust ratings with well preserved trust features. This is significant for large-scale trust rating transmission, trust evaluation and trust management. In this paper, we propose one greedy and two optimal multiple time interval (MTI) analysis algorithms. We also have studied the properties of our proposed algorithms analytically and empirically. These studies can illustrate that our algorithms can return a small set of MTI to represent a large set of trust ratings and preserve well the trust features.
  World Wide Web 10/2012; 15(5). · 0.51 Impact Factor