A generalized stereotype learning approach and its instantiation in trust modeling

Abstract

Owing to the lack of historical data regarding an entity in online communities, a user may rely on stereotyping to estimate its behavior based on historical data about others. However, these stereotypes cannot accurately reflect the user's evaluation if they are based on limited historical data about other entities. In view of this issue, we propose a novel generalized stereotype learning approach: the fuzzy semantic framework. Specifically, we propose a fuzzy semantic process, incorporated with traditional machine-learning techniques to construct stereotypes. It consists of two sub-processes: a fuzzy process that generalizes over non-nominal attributes (e.g., price) by splitting their values in a fuzzy manner, and a semantic process that generalizes over nominal attributes (e.g., location) by replacing their specific values with more general terms according to a predefined ontology. We also implement the proposed framework on the traditional decision tree method to learn users' stereotypes and validate the effectiveness of our framework for computing trust in e-marketplaces. Experiments on real data confirm that our proposed model can accurately measure the trustworthiness of sellers with which buyers have limited experience.