Predicting Trusts among Users of Online Communities - An Epinions Case Study

Author

Haifeng LIU
Ee-Peng LIM
Hady Wirawan LAUW, Singapore Management UniversityFollow
Minh-Tam LE
Aixin SUN
Jaideep SRIVASTAVA
Young Ae KIM

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2008

Abstract

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.

Discipline

Numerical Analysis and Scientific Computing | Theory and Algorithms

Publication

EC '08 Proceedings of the 9th ACM conference on Electronic commerce

First Page

310

Last Page

319

ISBN

978-1-60558-169-9

Identifier

10.1145/1386790.1386838

Publisher

ACM

City or Country

Chicago, USA

Citation

LIU, Haifeng; LIM, Ee-Peng; LAUW, Hady Wirawan; LE, Minh-Tam; SUN, Aixin; SRIVASTAVA, Jaideep; and KIM, Young Ae. Predicting Trusts among Users of Online Communities - An Epinions Case Study. (2008). EC '08 Proceedings of the 9th ACM conference on Electronic commerce. 310-319.
Available at: https://ink.library.smu.edu.sg/sis_research/3359

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

http://doi.org/10.1145/1386790.1386838