An Adaptive neuro-fuzzy Inference System to Evaluate Trustworthiness of Users in a Social Network
الموضوعات :
MohammadMahdi Shafiei
1
(Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran)
Hossein Shirgahi
2
(Department of Computer Engineering, Jouybar Branch, Islamic Azad University, Jouybar,Iran)
Homayun Motameni
3
(Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.)
Behnam Barzegar
4
(Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran)
الکلمات المفتاحية: Trust, Adaptive neuro-fuzzy inference system, social network,
ملخص المقالة :
In recent years, the emergence of various social networks has led to the growth of social network users. However, activity in such networks depends on the level of trust that users have in each other. Therefore, trust is essential and important issue in these networks, especially when users interact with each other. In this article, we examine this issue and provide a method to evaluate it. It is not easy to measure the accuracy of trust for users who interact with social networks. Here, interactions are virtual. In this article, we have used the adaptive neuro-fuzzy inference system to evaluate trustworthiness by considering different personality attributes of users such as reliability, availability, interest, patience and adaptability. Using these features as input and based on the adaptive neuro-fuzzy inference system, we evaluated the trustworthiness of users in social network. The proposed adaptive neuro-fuzzy inference system is expandable because in this system, trust can be defined as a set of one or more personality attributes. Epinions social network dataset is also used to simulate and validate the proposed method. In the proposed method, the absolute mean value of error is less than 0.0095 and the value of F-score is more than 0.9884. Based on the obtained results and compared to the previous methods, the proposed adaptive neuro-fuzzy inference system shows an acceptable accuracy for evaluating the trustworthiness of users.
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