Reputation Systems: Evaluating Reputation Among All Good Sellers

Jha, Vandana; Ramu, S. V.; Shenoy, P. Deepa; Venugopal, K R

doi:10.1007/s41688-017-0008-8

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…The trade-off between the benefit and the noise introduced by syntactic analysis is still difficult. Jha et al [16] 2017…”

Section: Literature Surveymentioning

confidence: 99%

A survey on sentimental cluster based opinion summarization in question answering community

Goswami¹

2019

IJATEE

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A sentiment analysis is a study which includes opinion mining, sentiment classification, and opinion summarization broadly. An opinion summarization plays an increasing research interest for automatically compressing the extensive information and generating a short summary with unlimited time. Opinion analysis is one of the emerging studies in computer domain which embrace of sentiment polarity, sentiment, opinion or semantic orientation. This paper presents the survey on sentiment analysis and summarization approaches with its challenges, methodology and pros and cons of the existing methodology. In this survey, we evaluated the research gaps of the existing technique for suggesting the new technique by the mean of applying the semi-supervised data undergo clustering; classification and summarization by means of convolutional neural network (CNN) network learning method which may use for the opinion summarization.

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“…The trade-off between the benefit and the noise introduced by syntactic analysis is still difficult. Jha et al [16] 2017…”

Section: Literature Surveymentioning

confidence: 99%

A survey on sentimental cluster based opinion summarization in question answering community

Goswami¹

2019

IJATEE

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A Framework for a Blockchain-Based Decentralized Data Marketplace

Aljohani¹,

Mukkamala²,

Olariu³

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Incremental collusive fraud detection in large-scale online auction networks

et al. 2020

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An online auction network (OAN) is a community of users who buy or sell items through an auction site. Along with the growing popularity of auction sites, concerns about auction frauds and criminal activities have increased. As a result, fraud detection in OANs has attracted renewed interest from researchers. Since most real OANs are large-scale networks, detecting fraudulent users is usually difficult, especially when multiple users collude with each other and new online auctions are continuously added. Although collusive auction frauds are not as popular as other types of auction frauds, they are more horrible and catastrophic because they often bring huge financial losses. To tackle this issue, some techniques have been proposed to detect collusive frauds in OANs. While all of the techniques have demonstrated promising results, they often suffer from low detection performance or slow convergence, especially in large-scale OANs. In this paper, we overcome these deficiencies by presenting ICAFD, a novel technique that recasts the problem of detecting collusive frauds in large-scale OANs as an incremental semi-supervised anomaly detection problem. In this technique, we propagate reputations from a small set of labeled benign users to unlabeled users along the auction relationships between them and then incrementally update reputations when a new auction gets added to the OAN. This increases the convergence of ICAFD and allows it to avoid wasteful recalculation of reputations from scratch. Our experimental results show that ICAFD can successfully detect different types of collusive auction frauds in a reasonable detection time.

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Reputation Systems: Evaluating Reputation Among All Good Sellers

Cited by 9 publications

References 48 publications

A survey on sentimental cluster based opinion summarization in question answering community

A survey on sentimental cluster based opinion summarization in question answering community

A Framework for a Blockchain-Based Decentralized Data Marketplace

Incremental collusive fraud detection in large-scale online auction networks

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