Predicting Trusts among Users of Online Communities - An Epinions Case Study
Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships...
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sg-smu-ink.sis_research-43612016-12-27T05:45:06Z Predicting Trusts among Users of Online Communities - An Epinions Case Study LIU, Haifeng LIM, Ee-Peng LAUW, Hady Wirawan LE, Minh-Tam SUN, Aixin SRIVASTAVA, Jaideep KIM, Young Ae Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data. 2008-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3359 info:doi/10.1145/1386790.1386838 https://ink.library.smu.edu.sg/context/sis_research/article/4361/viewcontent/PredictingTrust.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 Numerical Analysis and Scientific Computing Theory and Algorithms |
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Numerical Analysis and Scientific Computing Theory and Algorithms LIU, Haifeng LIM, Ee-Peng LAUW, Hady Wirawan LE, Minh-Tam SUN, Aixin SRIVASTAVA, Jaideep KIM, Young Ae Predicting Trusts among Users of Online Communities - An Epinions Case Study |
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Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data. |
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LIU, Haifeng LIM, Ee-Peng LAUW, Hady Wirawan LE, Minh-Tam SUN, Aixin SRIVASTAVA, Jaideep KIM, Young Ae |
author_facet |
LIU, Haifeng LIM, Ee-Peng LAUW, Hady Wirawan LE, Minh-Tam SUN, Aixin SRIVASTAVA, Jaideep KIM, Young Ae |
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LIU, Haifeng |
title |
Predicting Trusts among Users of Online Communities - An Epinions Case Study |
title_short |
Predicting Trusts among Users of Online Communities - An Epinions Case Study |
title_full |
Predicting Trusts among Users of Online Communities - An Epinions Case Study |
title_fullStr |
Predicting Trusts among Users of Online Communities - An Epinions Case Study |
title_full_unstemmed |
Predicting Trusts among Users of Online Communities - An Epinions Case Study |
title_sort |
predicting trusts among users of online communities - an epinions case study |
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Institutional Knowledge at Singapore Management University |
publishDate |
2008 |
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https://ink.library.smu.edu.sg/sis_research/3359 https://ink.library.smu.edu.sg/context/sis_research/article/4361/viewcontent/PredictingTrust.pdf |
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