Vinicius Almendra

Learn More
Online auction sites have to deal with a enormous amount of product listings, of which a fraction is fraudulent. Although small in proportion, fraudulent listings are costly for site operators, buyers and legitimate sellers. Fraud prediction in this scenario can benefit significantly from machine learning techniques, although interpretability of model(More)
One of the most important problems on the semantic web area is the one of trust. The growing exchange of semantic web data raises the need of policies that allow filtering out untrustworthy information. It is necessary, however , to model adequately the concept of trustworthiness, otherwise one may end up with operational trust measures that lack a clear(More)
Fraud is a recurring phenomenon at online actions sites like eBay. The enormous amount of transaction data public ally available offers a good opportunity for fraud prevention based on learning methods. However, online auction sites usually neither confirm nor deny fraudulent behavior: they simply suspend seller accounts and publicize feedback information(More)
Online auction fraud is the most common complaint according to Internet Crime and Complaint Center. Despite that, there are not many published empirical studies about fraud occurrence in online auction sites, and the existing ones mostly target eBay. This lack of research is even worse in Latin American countries. In this paper we present the results of an(More)
Outlier detection is a relevant problem for many domains, among which for detection of fraudulent behavior. Exploratory Data Analysis techniques are known to be very useful for highlighting patterns and deviations in data through visual representations. Less explored is the feasibility of using them to build learning models for outlier detection, which can(More)
Non-delivery fraud is a recurring problem at online auction sites: false sellers that list nonexistent products just to receive payments and afterwards disappear, possibly repeating the swindle with another identity. In our work we identified a set of publicly available features related to listings, sellers and product categories, and built a machine(More)
Research dissemination in the Computer Science domain depends heavily on conference publications. The review processes of major conferences is rigorous and the work presented in those venues have more visibility and more citations than many journals, with the advantage of a faster dissemination of ideas. We consider that any evaluation system in the(More)
Non-delivery fraud is a recurring problem at online auction sites: false sellers that list inexistent products just to receive payments and disappear, possibly repeating the swindle with another identity. The high transaction volume of these sites calls for the use of machine learning techniques in fraud prediction systems, at least for the identification(More)
  • 1