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Predicting trust from user ratings

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dc.contributor.author Korovaiko, Nikolay
dc.date.accessioned 2011-12-13T23:39:15Z
dc.date.available 2011-12-13T23:39:15Z
dc.date.copyright 2011 en_US
dc.date.issued 2011-12-13
dc.identifier.uri http://hdl.handle.net/1828/3722
dc.description.abstract Trust relationships between users in various online communities are notoriously hard to model for computer scientists. It can be easily verified that trying to infer trust based on the social network alone is often inefficient. Therefore, the avenue we explore is applying Data Mining algorithms to unearth latent relationships and patterns from background data. In this paper, we focus on a case where the background data is user ratings for online product reviews. We consider as a testing ground a large dataset provided by Epinions.com that contains a trust network as well as user ratings for reviews on products from a wide range of categories. In order to predict trust we define and compute a critical set of features, which we show to be highly effective in providing the basis for trust predictions. Then, we show that state-of-the-art classifiers can do an impressive job in predicting trust based on our extracted features. For this, we employ a variety of measures to evaluate the classification based on these features. We demonstrate that by carefully collecting and synthesizing readily available background information, such as ratings for online reviews, one can accurately predict trust-based social links. en_US
dc.language English eng
dc.language.iso en en_US
dc.subject trust prediction en_US
dc.subject social networks en_US
dc.subject data mining en_US
dc.subject algorithms en_US
dc.subject models en_US
dc.title Predicting trust from user ratings en_US
dc.type Thesis en_US
dc.contributor.supervisor Thomo, Alex
dc.degree.department Dept. of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.rights.temp Available to the World Wide Web en_US
dc.description.scholarlevel Graduate en_US


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