Trust Network Inference for Online Rating Data Using Generative Models
Conference Proceeding Article
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
City or Country
CHUA, Freddy Tat Chua and LIM, Ee Peng.
Trust Network Inference for Online Rating Data Using Generative Models. (2011). KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 889-898. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/620