  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. 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. 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 precomputation of aggregates over large-scale ratings and
transaction data of three context dimensions as well as new data structures for
appropriately indexing aggregation results to promptly answer buyers' CTT
requests. To solve these challenging problems, we then propose a new index
scheme CMK-tree. After that, we further extend CMK-tree and propose a
CMK-treeRS approach to reducing the storage space allocated to each seller.
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-treeRS
approach can further improve the performance in answering buyers' CTT queries.
