Using Stereotypes to Identify Risky Transactions in Internet Auctions

Liu, Xin; Kaszuba, Tomasz; Nielek, Radosław; Datta, Anwitaman; Wierzbicki, Adam

doi:10.1109/socialcom.2010.81

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…This is akin to the clustering approach that Liu et al . (2009, 2010, 2013) proposed in StereoTrust, where interactions are separated into groups defined by observed traits. The interactions in groups that match the current trustee traits are then aggregated to produce an overall trust value.…”

Section: Research Landscapementioning

confidence: 99%

“…As with interaction context, one method for considering stereotype information is to use ratings from historical interaction records where similar traits were observed. This is akin to the clustering approach that Liu et al (2009Liu et al ( , 2010Liu et al ( , 2013 proposed in StereoTrust, where interactions are separated into Reputation assessment groups defined by observed traits. The interactions in groups that match the current trustee traits are then aggregated to produce an overall trust value.…”

Section: Representation Of Trust Informationmentioning

confidence: 99%

See 1 more Smart Citation

Reputation assessment: a review and unifying abstraction

Taylor

Barakat

Miles

et al. 2018

The Knowledge Engineering Review

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Trust and reputation allow agents to make informed decisions about potential interactions. Trust in an agent is derived from direct experience with that agent, while reputation is determined by the experiences reported by other witness agents with potentially differing viewpoints. These experiences are typically aggregated in a trust and reputation model, of which there are several types that focus on different aspects. Such aspects include handling subjective perspectives of witnesses, dishonesty, or assessing the reputation of new agents. In this paper, we distil reputation systems into their fundamental aspects, discussing first how trust and reputation information is represented and second how it is disseminated among agents. Based on these discussions, a unifying abstraction is presented for trust and reputation systems, which is demonstrated by instantiating it with a broad range of reputation systems found in the literature. The abstraction is then instantiated to combine the range of capabilities of existing reputation systems in the Machine Learning Reputation System, which is evaluated using a marketplace simulation.

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Section: Research Landscapementioning

confidence: 99%

Section: Representation Of Trust Informationmentioning

confidence: 99%

Reputation assessment: a review and unifying abstraction

Taylor

Barakat

Miles

et al. 2018

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Similarly to our Stereotype-Sharing Overlay Network, in their work, new trustors can request stereotypes from the experienced ones, and then combine these stereotypes with the target agent's reputation (if any) using subjective logic [15]. Although similar, our approach has several advantages: (1) as a basic trust model, StereoTrust uses the (well adapted) beta distribution to derive stereotypes and trust, thus the resulting decision is easy to be interpreted; (2) The work [7] shares agents' local stereotypes heuristically (e.g., no discussion on how stereotype providers are selected) while StereoTrust offers a more sophisticated sharing mechanism by maintaining a dedicated overlay network for exchanging, combining, and updating stereotypes; (3) StereoTrust is applicable in practice as demonstrated in real-world dataset (Epinions.com) based evaluation (see Section 8) presented in this paper, as well as in other diverse applications and settings in which we have applied stereotype to derive trust [22,25].…”

Section: Related Workmentioning

confidence: 99%

Trust beyond reputation: A computational trust model based on stereotypes

Liu

Datta

Rządca

2013

Electronic Commerce Research and Applications

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Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"-essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.

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“…A buyer can then use its own past transactions (with other sellers) to form stereotypes according to the identified attributes. Both StereoTrust and MetaTrust are used to derive stereotypes based trust [129].…”

Section: Chapter 6 Identifying Risky Transactions In Internet Auctionsmentioning

confidence: 99%

“…long). Inspired by behavior science and psychology, as well as leveraging on observations from existing literature [148,129], we study four potentially useful features, which will be discussed and verified in the next subsections. Note that there are possibly other useful features which can further improve the results, or replace some of the chosen ones.…”

Section: Featuresmentioning

confidence: 99%

Trust beyond reputation : novel trust mechanisms for distributed environments

Liu¹

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Using Stereotypes to Identify Risky Transactions in Internet Auctions

Cited by 9 publications

References 18 publications

Reputation assessment: a review and unifying abstraction

Reputation assessment: a review and unifying abstraction

Trust beyond reputation: A computational trust model based on stereotypes

Trust beyond reputation : novel trust mechanisms for distributed environments

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