Fraud detection in reputation systems in e-markets using logistic regression

Maranzato, Rafael; Pereira, Andréa Franco; Lago, Alair Pereira do; Neubert, Marden

doi:10.1145/1774088.1774400

Cited by 29 publications

(20 citation statements)

References 12 publications

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“…The probability of negative feedback is an intuitive evaluation metric that presents as an important characteristic to be more trustable than the positive feedback in most of the e-markets, since typical attacks try to inflate the sellers' reputation through positive feedbacks [10]. As long as the selected metric considers only information about negative feedbacks, which can assumed to be trustworthy, it can provide a more accurate analysis of the quality of credibility models.…”

Section: Methodsmentioning

confidence: 99%

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Modeling and evaluating credibility of web applications

Guimarães

Pereira

Silva

et al. 2011

Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality

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The popularization of the Web has given rise to new services every day, demanding mechanisms to ensure the credibility of these services. In this work we adopt a framework for the design, implementation and evaluation of credibility models. We call a credibility model a function capable of assigning a credibility value to a transaction of a Web application, considering different criteria of this service and its supplier. To validate this framework and models, we perform experiments using an actual dataset, from which we evaluated different credibility models using distinct types of information. The obtained results are very good, showing representative gains, when compared to a baseline and also to a known state-ofthe-art approach. The results show that the credibility framework can be used to enforce trust to users of services on the Web.

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Section: Methodsmentioning

confidence: 99%

“…Moreover, there are specific researches that deal with improving the quality of reputation systems, such as the identification of fraudsters of these systems [10], which was also used in the same real application used in this research.…”

Section: Introductionmentioning

confidence: 99%

Modeling and evaluating credibility of web applications

Guimarães

Pereira

Silva

et al. 2011

Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality

Self Cite

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“…Some marketplaces, such as Amazon 1 and Taobao 2 , have reached great popularity and high level revenue, emerging as a very relevant model in the Business-to-Consumer (B2C) and Consumer-to-Consumer (C2C) e-commerce scenario [1]. It is convenient for people to purchase or return products through the Internet.…”

Section: Introductionmentioning

confidence: 99%

Based Big Data Analysis of Fraud Detection for Online Transaction Orders

Yang

Cheng

et al. 2015

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

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Abstract. Fraud control is important for the online marketplace. This study addresses the problem of detecting attempts to deceive orders in Internet transactions. Our goal is to generate an algorithm model to detect and prevent the fraudulent orders. First, after analyzing the real historical data of customers' orders from Dangdang Website (http://www.dangdang.com. E-commerce China Dangdang Inc (Dangdang) is a leading e-commerce company in China. Dangdang officially listed on the New York Stock Exchange on December 8th, 2010, and is the first Chinese B2C e-commerce company which is completely based on online business to list on New York Stock.), we described characteristics related to transactions that may indicate frauds orders. We presented fraudulent orders characteristic matrix through comparing the normal and abnormal orders. Secondly, we apply Logic Regression model to identify frauds based on the characteristic matrix. We used real data from Dang company to train and evaluate our methods. Finally we evaluated the validity of solutions though analyzing feedback data.

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“…The rapid development of the Internet has accelerated the e-commerce upsurge [1]. Nowadays, network shopping has become a convenient and relatively new way of purchasing, which is chosen by increasingly more people.…”

Section: Introductionmentioning

confidence: 99%

Clothing Product Reviews Mining Based on Machine Learning

Hong¹,

Feng²,

Cheng³

2015

Int. J. Onl. Eng.

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Abstract-This paper used the method of machine learning to study clothing product reviews classification based on big enterprise data. Taking Taobao clothing reviews as the object, it firstly excavated review themes from reviews corpus by association rules, and then searched review themes related to the categories by a method of mutual information to enrich the review themes. In the process of building classification models, commonly used SVM classifiers were studied in the beginning. After training and verification of a large amount of data, the classification accuracy reached 90.597%. In order to further improve the classification accuracy, the maximum entropy model was built by adopting the maximum entropy algorithm, on the basis of the same review themes. After repeated experiments and optimization in a large-scale of clothing product reviews, the classification accuracy reached up to 93.035% finally. Compared with SVM classification algorithm, the accuracy of maximum entropy in the clothing product reviews classification is higher. This paper verified the effectiveness of maximum entropy model on comment text multi-classification problem, and the maximum entropy model has practical values in electronic business.Index Terms-mutual information, review classification, SVM, the maximum entropy.

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Fraud detection in reputation systems in e-markets using logistic regression

Cited by 29 publications

References 12 publications

Modeling and evaluating credibility of web applications

Modeling and evaluating credibility of web applications

Based Big Data Analysis of Fraud Detection for Online Transaction Orders

Clothing Product Reviews Mining Based on Machine Learning

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