NDLI: Trust prediction from user-item ratings

Content Provider	Springer Nature Link
Author	Korovaiko, Nikolay Thomo, Alex
Copyright Year	2013
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 are 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 show that by carefully collecting and synthesizing readily available background information, such as ratings for online reviews, one can accurately predict social links based on trust.
Starting Page	749
Ending Page	759
Page Count	11
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	3
Issue Number	3
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2013-06-25
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Trust Prediction Trust Inference Epinions Data mining Data Mining and Knowledge Discovery Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences
Content Type	Text
Resource Type	Article
Subject	Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology

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