pp 1–15 | Cite as

Presenting a hybrid model in social networks recommendation system architecture development

  • Abolfazl ZareEmail author
  • Mohammad Reza Motadel
  • Aliakbar Jalali
Student Forum


There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity.


Recommendation systems Collaborative filtering Artificial neural network Fuzzy logic Supply-chain management Social networks 



Recommendation systems


Supply-chain management


Artificial neural networks


Collaborative filtering RS


Demographic RS


Content-based RS


Data mining


Euclidean distance


Membership function


Mean absolute error


Root mean squared error


False-positive rate


True-positive rate


Overlap measure



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management, Central Tehran BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

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