A generalized stereotype learning approach and its instantiation in trust modeling

Name: A generalized stereotype learning approach and its instantiation in trust modeling
Author: Fang, H., Zhang, J., Şensoy, Murat

İsim	A generalized stereotype learning approach and its instantiation in trust modeling
Yazar	Fang, H., Zhang, J., Şensoy, Murat
Basım Tarihi:	2018-08
Basım Yeri	- Elsevier
Konu	User modeling, Stereotype trust model, Fuzzy semantic framework, E-commerce
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1567-4223
Kayıt Numarası	34c5ef3b-2a76-46c5-a1d1-2365f0163ebe
Lokasyon	Computer Science
Tarih	2018-08
Notlar	National Natural Science Foundation of China ; Basic Academic Discipline Program for Shanghai University of Finance and Economics
Örnek Metin	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

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A generalized stereotype learning approach and its instantiation in trust modeling

Yazar Fang, H., Zhang, J., Şensoy, Murat

Basım Tarihi 2018-08

Basım Yeri - Elsevier

Konu User modeling, Stereotype trust model, Fuzzy semantic framework, E-commerce

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1567-4223

Kayıt Numarası 34c5ef3b-2a76-46c5-a1d1-2365f0163ebe

Lokasyon Computer Science

Tarih 2018-08

Notlar National Natural Science Foundation of China ; Basic Academic Discipline Program for Shanghai University of Finance and Economics

Örnek Metin 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