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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Numerical Analysis and Scientific Computing

Singapore Management University

Series

2009

Trust prediction

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun Dec 2009

To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun

Research Collection School Of Computing and Information Systems

This paper analyzes the trustor and trustee factors that lead to inter-personal trust using a well studied Trust Antecedent framework in management science. To apply these factors to trust ranking problem in online rating systems, we derive features that correspond to each factor and develop different trust ranking models. The advantage of this approach is that features relevant to trust can be systematically derived so as to achieve good prediction accuracy. Through a series of experiments on real data from Epinions, we show that even a simple model using the derived features yields good accuracy and outperforms MoleTrust, a trust …


Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun Nov 2009

Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun

Research Collection School Of Computing and Information Systems

Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments …