ReputationPro: The Efficient Approaches to CTT Computation in E-Commerce Environments

In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when the seller is unknown to them. Most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context information into account. With such a result as the indication of reputation, a buyer may be easily deceived by a malicious seller in a transaction where the notorious value imbalance problem is involved, namely, a malicious seller accumulates a high level reputation by selling cheap products then deceives buyers by inducing them to purchase more expensive products. In this paper, we first present a trust vector consisting of three values for Contextual Transaction Trust (CTT). In the computation of three CTT values, the identified three important context dimensions, including product category, transaction amount and transaction time, are taken into account. Meanwhile, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters regarding context dimensions that are specified by a buyer, different sets of CTT values can be calculated. As a result, all these values can outline the reputation profile of a seller that indicates the dynamic trust levels of a seller in different product categories, price ranges, time periods, and any necessary combination of them. We term this new model as ReputationPro. However, in ReputationPro, the computation of reputation profile requires novel algorithms for the pre-computation of aggregates over large-scale ratings and ∗Mr. Zhang is the corresponding author of this paper transaction data of three context dimensions as well as new data structures for appropriately indexing aggregation results to promptly answer buyers’ CTT requests. Moreover, storing pre-computed aggregation results needs to consume a large volume of space particularly for a system with millions sellers. Therefore, reducing storage space for aggregation results is also a challenging demand. To solve these challenging problems, we then propose a new index scheme CMK-tree by extending the K-D-B-tree that indexes spatial data to support efficient computation of CTT values. After that, we further extend CMK-tree and propose a CMK-tree approach to reducing the storage space allocated to each seller. The two approaches not only are applicable to three context dimensions that are either linear or hierarchical, but also take into account the characteristics of the transaction-time model, namely, transaction data is inserted in nondecreasing time order. Moreover, the proposed data structures can index each specific product traded in a time period in order to compute the trust level of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency for computing CTT values to all three existing approaches in the literature. In addition, though with reduced storage space, the CMK-tree approach can further improve the performance in answering buyers’ CTT queries. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce websites in terms of efficiency and storage space consumption.