Predicting trust from user ratings

dc.contributor.authorKorovaiko, Nikolay
dc.contributor.supervisorThomo, Alex
dc.date.accessioned2011-12-13T23:39:15Z
dc.date.available2011-12-13T23:39:15Z
dc.date.copyright2011en_US
dc.date.issued2011-12-13
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractTrust 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.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/3722
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjecttrust predictionen_US
dc.subjectsocial networksen_US
dc.subjectdata miningen_US
dc.subjectalgorithmsen_US
dc.subjectmodelsen_US
dc.titlePredicting trust from user ratingsen_US
dc.typeThesisen_US

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