Author
Fang, H., Zhang, J., Şensoy, Murat
Publication Date
2018-08
Publication Place
-
Elsevier
Subject
User modeling, Stereotype trust model, Fuzzy semantic framework, E-commerce
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1567-4223
Record ID
34c5ef3b-2a76-46c5-a1d1-2365f0163ebe
Library Location
Computer Science
Date
2018-08
Notes
National Natural Science Foundation of China ; Basic Academic Discipline Program for Shanghai University of Finance and Economics
Sample Text
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.
DOI
10.1016/j.elerap.2018.06.004
Cilt
30
