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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| Main Authors: | , , , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2008
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| Subjects: | |
| Online Access: | 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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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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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