Trust relationship prediction using online product review data

Authors:
Nan Ma;Ee-Peng Lim;Viet-An Nguyen;Aixin Sun;Haifeng Liu
Affiliations:
Nanyang Technological University, Singapore, Singapore;Singapore Management University, Singapore, Singapore;Singapore Management University, Singapore, Singapore;Nanyang technological university, Singapore, Singapore;IBM Research China, Beijing, China
Venue:
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Year:
2009

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Abstract

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 using two datasets from Epinions.com. It is shown that the personalized and cluster-based classification methods outperform the global classification method, particularly for the active users.