Relating Reputation and Money in Online Markets

Swaminathan, Ashwin; Cattelan, Renan G.; Wexler, Ydo; Mathew, Cherian V.; Kirovski, Darko

doi:10.1145/1841909.1841914

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…In addition, it does not support the trust evaluation of a seller in the transactions in a given price range. This need comes from a buyer when he or she is concerned about the risk of monetary loss in a forthcoming transaction [Xiong and Liu 2004;Swaminathan et al 2010]. By contrast, the ReputationPro model can outline a seller's reputation profile and indicate his or her dynamic trustworthiness in different products, product categories, price ranges, and time periods.…”

Section: Context-aware Trust Evaluationmentioning

confidence: 99%

ReputationPro

et al. 2015

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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-tree RS 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...

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Section: Context-aware Trust Evaluationmentioning

confidence: 99%

ReputationPro

et al. 2015

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“…The network can manage the payments to agents who are not direct trusters of an issuer by finding a trust path where every agent accepts the currency of the previous on the path and a sequence of currency exchanges leads to an effective payment. In effect, one can use the same mechanism that I use in Section 5 to locate trusted transaction partners to pay for transactions (Swaminathan, 2010).…”

Section: Applicationmentioning

confidence: 99%

“…I focus on designing an architecture that facilitates and even guarantees trust and not on explaining the trust formation in existing architectures. However, understanding the concept of trust, classification of trust types, and the impact of context on trust are relevant to this discussion (Benbasat, Gefen, & Pavlou;Pavlou & Geffen, 2004;Swaminathan, 2010;Wang & Benbasat, 2008). Fourth, incentives are critical in building trust networks both to encourage participation and also to discourage dishonesty and nonperformance (Ba, 2001;Pavlou & Geffen, 2004;Söllner, Hoffmann, Hoffmann, Wacker, & Leimeister).…”

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confidence: 99%

The Design of Trust Networks

Orman¹

2015

CAIS

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One can use trust networks to find trustworthy information, people, products, and services on public networks. Hence, they have the potential to combine the advantages of search, recommendation systems, and social networks. But proper design and correct incentives are critical to the success of such networks. In this paper, I propose a trust network architecture that emphasizes simplicity and robustness. I propose a trust network with constrained trust relationships and design a decentralized search and recommendation process. I create both informational and monetary incentives to encourage joining the network, to investigate and discover other trustworthy agents, and to make commitments to them by trusting them, by insuring them, or even by directly investing in them. I show that making the correct judgments about trustworthiness of others and reporting it truthfully are the optimum strategies since they reward the agents both with information by providing access to more of the network and with monetary payments by paying them for their services as information intermediaries. The extensive income potential from the trust connections creates strong incentives to join the network, to create reliable trust connections, and to report them truthfully.

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Information markets over trust networks

Orman

2016

Electron Commer Res

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Relating Reputation and Money in Online Markets

Cited by 4 publications

References 24 publications

ReputationPro

ReputationPro

The Design of Trust Networks

Information markets over trust networks

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