Title

Trust Network Inference for Online Rating Data Using Generative Models

Publication Type

Conference Proceeding Article

Publication Date

7-2011

Abstract

In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

First Page

889

Last Page

898

ISBN

9781450300551

Identifier

10.1145/1835804.1835917

Publisher

ACM

City or Country

Washington D.C.

Additional URL

http://dx.doi.org/10.1145/1835804.1835917

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