ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments

In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness of a seller without taking any transaction context...

Full description

Saved in:
Bibliographic Details
Main Authors: ZHANG, Haibin, WANG, Yan, ZHANG, Xiuzhen, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/2526
https://ink.library.smu.edu.sg/context/sis_research/article/3526/viewcontent/ReputationPro_av.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-3526
record_format dspace
spelling sg-smu-ink.sis_research-35262020-01-15T05:36:51Z ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments ZHANG, Haibin WANG, Yan ZHANG, Xiuzhen LIM, Ee Peng In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness 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—in other words, a malicious seller accumulates a high-level reputation by selling cheap products and then deceives buyers by inducing them to purchase more expensive products. In this article, we first present a trust vector consisting of three values for contextual transaction trust (CTT). In the computation of CTT values, three identified important context dimensions, including Product Category, Transaction Amount, and Transaction Time, are taken into account. In the meantime, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters specified by a buyer regarding context dimensions, different sets of CTT values can be calculated. As a result, all of these trust values can outline the reputation profile of a seller that indicates the dynamic trustworthiness of a seller in different products, product categories, price ranges, time periods, and any necessary combination of them. We name this new model ReputationPro. Nevertheless, in ReputationPro, the computation of reputation profile requires new data structures for appropriately indexing the precomputation of aggregates over large-scale ratings and transaction data in three context dimensions, as well as novel algorithms for promptly answering buyers’ CTT queries. In addition, storing precomputed aggregation results consumes a large volume of space, particularly for a system with millions of sellers. Therefore, reducing storage space for aggregation results is also a great demand. To solve these challenging problems, we first propose a new index scheme CMK-tree by extending the two-dimensional K-D-B-tree that indexes spatial data to support efficient computation of CTT values. Then, we further extend the CMK-tree and propose a CMK-treeRS approach to reducing the storage space allocated to each seller. The two approaches are not only applicable to three context dimensions that are either linear or hierarchical but also take into account the characteristics of the transaction-time model—that is, transaction data is inserted in chronological order. Moreover, the proposed data structures can index each specific product traded in a time period to compute the trustworthiness of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency of computing CTT values to all three existing approaches in the literature. In particular, while answering a buyer’s CTT queries for each brand-based product category, the CMK-tree has almost linear query performance. In addition, with significantly reduced storage space, the CMK-treeRS approach can further improve the efficiency in computing CTT values. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce Web sites in terms of efficiency and storage space consumption. 2015-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2526 info:doi/10.1145/2697390 https://ink.library.smu.edu.sg/context/sis_research/article/3526/viewcontent/ReputationPro_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems E-Commerce
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
E-Commerce
spellingShingle Computer Sciences
Databases and Information Systems
E-Commerce
ZHANG, Haibin
WANG, Yan
ZHANG, Xiuzhen
LIM, Ee Peng
ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
description In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness 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—in other words, a malicious seller accumulates a high-level reputation by selling cheap products and then deceives buyers by inducing them to purchase more expensive products. In this article, we first present a trust vector consisting of three values for contextual transaction trust (CTT). In the computation of CTT values, three identified important context dimensions, including Product Category, Transaction Amount, and Transaction Time, are taken into account. In the meantime, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters specified by a buyer regarding context dimensions, different sets of CTT values can be calculated. As a result, all of these trust values can outline the reputation profile of a seller that indicates the dynamic trustworthiness of a seller in different products, product categories, price ranges, time periods, and any necessary combination of them. We name this new model ReputationPro. Nevertheless, in ReputationPro, the computation of reputation profile requires new data structures for appropriately indexing the precomputation of aggregates over large-scale ratings and transaction data in three context dimensions, as well as novel algorithms for promptly answering buyers’ CTT queries. In addition, storing precomputed aggregation results consumes a large volume of space, particularly for a system with millions of sellers. Therefore, reducing storage space for aggregation results is also a great demand. To solve these challenging problems, we first propose a new index scheme CMK-tree by extending the two-dimensional K-D-B-tree that indexes spatial data to support efficient computation of CTT values. Then, we further extend the CMK-tree and propose a CMK-treeRS approach to reducing the storage space allocated to each seller. The two approaches are not only applicable to three context dimensions that are either linear or hierarchical but also take into account the characteristics of the transaction-time model—that is, transaction data is inserted in chronological order. Moreover, the proposed data structures can index each specific product traded in a time period to compute the trustworthiness of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency of computing CTT values to all three existing approaches in the literature. In particular, while answering a buyer’s CTT queries for each brand-based product category, the CMK-tree has almost linear query performance. In addition, with significantly reduced storage space, the CMK-treeRS approach can further improve the efficiency in computing CTT values. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce Web sites in terms of efficiency and storage space consumption.
format text
author ZHANG, Haibin
WANG, Yan
ZHANG, Xiuzhen
LIM, Ee Peng
author_facet ZHANG, Haibin
WANG, Yan
ZHANG, Xiuzhen
LIM, Ee Peng
author_sort ZHANG, Haibin
title ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
title_short ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
title_full ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
title_fullStr ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
title_full_unstemmed ReputationPro: The efficient approaches to contextual transaction trust computation in e-commerce environments
title_sort reputationpro: the efficient approaches to contextual transaction trust computation in e-commerce environments
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2526
https://ink.library.smu.edu.sg/context/sis_research/article/3526/viewcontent/ReputationPro_av.pdf
_version_ 1770572478529142784