Trust network inference for online rating data using generative models
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 t...
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sg-smu-ink.sis_research-16192018-06-19T07:53:27Z Trust network inference for online rating data using generative models CHUA, Freddy Tat Chua LIM, Ee Peng 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. 2011-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/620 info:doi/10.1145/1835804.1835917 https://ink.library.smu.edu.sg/context/sis_research/article/1619/viewcontent/p889_chua.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing |
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Databases and Information Systems Numerical Analysis and Scientific Computing CHUA, Freddy Tat Chua LIM, Ee Peng Trust network inference for online rating data using generative models |
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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. |
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text |
author |
CHUA, Freddy Tat Chua LIM, Ee Peng |
author_facet |
CHUA, Freddy Tat Chua LIM, Ee Peng |
author_sort |
CHUA, Freddy Tat Chua |
title |
Trust network inference for online rating data using generative models |
title_short |
Trust network inference for online rating data using generative models |
title_full |
Trust network inference for online rating data using generative models |
title_fullStr |
Trust network inference for online rating data using generative models |
title_full_unstemmed |
Trust network inference for online rating data using generative models |
title_sort |
trust network inference for online rating data using generative models |
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Institutional Knowledge at Singapore Management University |
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2011 |
url |
https://ink.library.smu.edu.sg/sis_research/620 https://ink.library.smu.edu.sg/context/sis_research/article/1619/viewcontent/p889_chua.pdf |
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