A Novel Model for Contextual Transaction Trust Computation with Fixed Storage Space in E-Commerce and E-Service Environments

In e-commerce and e-service environments, transaction context is important when evaluating the trust level of a seller or a service provider in a forthcoming transaction. However, most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context into account. In the literature, a trust vector approach has been proposed to resolve the above problem. In particular, the trust vector contains different sets of trust values (termed as CTT values) so as to outline a seller's reputation profile. As a result, buyers can identify the potential risk existing in a forthcoming transaction (e.g., value imbalance, i.e. a malicious seller may build up a high level of trust by selling cheap products and then deceive buyers by inducing them to purchase more expensive products) and thus avoid monetary losses. In computing CTT values, some approaches are proposed that store the precomputed aggregation results over large-scale ratings and transaction data of a seller, so as to deliver prompt responses to a buyer's query. Though these approaches allocate relatively small space to each seller for storing the aggregation results, if applied in a system with millions of sellers, space consumption will be intolerable. In this paper, we propose a novel model for CTT computation with fixed storage space, which provides a trade-off between aggregation detail and storage space. It is particular suitable for CTT computation where a request is regarding a seller's trust in recent time period, e.g., the latest six months, rather than six months plus one day. We have conducted experiments on both an eBay dataset and a synthetic dataset to illustrate its good efficiency in responding to buyers' CTT queries.