This research paper introduces a novel approach to predicting trust between users in online communities, crucial for decision-making in environments like e-commerce and product review websites. Unlike prior works that rely on the sparse connectivity of known trust ratings, this study adopts a classification method, employing a comprehensive taxonomy to derive a wide range of features from user attributes and interactions within the community. The methodology was tested on data from Epinions, a large product review platform featuring various types of user interactions and a trust network. The findings demonstrate the effectiveness of using pre-trained classifiers to predict trust among users, offering a significant advancement in trust prediction within online communities.
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Liu, Haifeng, et al. "Predicting trusts among users of online communities: an epinions case study." Proceedings of the 9th ACM Conference on Electronic Commerce. 2008.
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