To Trust or Not to Trust? Predicting Online Trusts Using Trust Antecedent Framework
Conference Proceeding Article
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 propagation based model. SVM classifiers using these features also show improvements.
Trust prediction, trust ranking, trust antecedent framework
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
IEEE International Conference on Data Mining
City or Country
Miami, Florida, Dec 6-9
Viet-An Nguyen, Ee-Peng Lim, Jing Jiang, Aixin Sun. 2009. "To Trust or Not to Trust? Predicting Online Trusts Using Trust Antecedent Framework." Ninth IEEE International Conference on Data Mining, 896-901.