Special Issue on Computational Intelligence Paradigms in Recommender Systems and Online Social Networks

in Special Issue   Posted on August 11, 2017 

Information for the Special Issue

Submission Deadline: Sun 31 Dec 2017
Journal Impact Factor : 2.644
Journal Name : Journal of Computational Science
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/journal-of-computational-science/call-for-papers/special-issue-on-computational-intelligence-paradigms-in-rec
Journal & Submission Website: https://www.journals.elsevier.com/journal-of-computational-science

Special Issue Call for Papers:

Computational Intelligence encompasses a number of nature-inspired computational methodologies, mainly artificial neural networks (ANNs), fuzzy sets, genetic algorithms (GAs), and their hybridizations, such as neuro-fuzzy computing and neo-fuzzy systems, for addressing real-world problems to which conventional modelling can be useless due to several reasons such as complexity, existent of uncertainties, and the stochastic nature of the processes. Computational Intelligence is a powerful methodology for a wide range of data analysis problems such as financial forecasting, industrial, scientific, and social media applications. The recent advances in computational intelligence have shown very promising results in industry, business, sciences and social media studies. Meanwhile, the online social networks (OSNs) such as Facebook, LinkedIn, Twitter, and Instagram have become very popular and attracted many users from all around the world. Recommender systems in combination with OSNs have also produced new business opportunities, making the social impact of OSNs more critical for product marketing, establishing new connections and improving the user’s experience by personalization of the user’s contents. This has led to new diverse challenges for practitioners and researchers of OSNs and recommender systems in terms of large-scale social network interactions and diversity of social media data from a multitude of OSNs. Given the success of computational intelligence methods and techniques in big data analysis applications, it is expected that they can also be applied successfully in the analysis of large-scale raw data in OSNs. In this context, computational intelligence paradigms comprising of numerous branches including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems, can play a vital role in handling the different aspects of OSNs and recommender systems.


In this special issue, we invite researchers to contribute high-quality articles and surveys focusing on computational intelligence methods for recommenders systems and OSNs. The relevant topics of this special issue include but are not limited to:

  1. Computational intelligence solutions for OSNs and recommendation in recommender systems
  2. Computational intelligence in mobile-cloud based computing for social network recommendation services
  3. Big data analytics for community activity prediction, management, and decision-making in OSNs
  4. Fuzzy system theory in OSNs and recommender systems
  5. Social data analytical approaches using computational methods
  6. Deep learning and machine learning algorithms for efficient indexing and retrieval in multimedia recommendation systems and OSNs
  7. Intelligent techniques for smart surveillance and security in OSNs
  8. Modeling, data mining, and public opinion analysis based on social big data
  9. Crowd computing-assisted access control and digital rights management for OSNs
  10. Evolutionary algorithms for data analysis and recommendations
  11. Crowd intelligence and computing paradigms for sentimental analysis and recommendation
  12. Applied soft computing for content security, vulnerability and forensics in OSNs
  13. Computational intelligence in multimedia computing and context-aware recommendation
  14. Scalable, incremental learning and understanding of OSN big data with its real-world applications for visualization, HCI, and virtual reality community
  15. Crowd intelligence-assisted ubiquitous, personal, and mobile social media applications
  16. Recommender systems for crowdsourcing and privacy preserving crowdsourcing
  17. Crowdsourcing and crowd sensing based on OSN and its applications for trust evaluation
  18. Artificial intelligence and pattern recognition technologies for recommendation in healthcare
  19. Deep learning and computational intelligence based medical data analysis for recommendation and smart healthcare services


Important Dates

Submission Deadline: 30th December, 2017

Feedback to Authors after Initial Screening: 1st February, 2018

Review Results Notification: 1st April, 2018

Revised Manuscript Due: 20th May, 2018

Final Decision Notification: 15th July, 2018


Submission Guidelines

Please use the electronic submission system at https://www.evise.com/evise/jrnl/jocs, and select "SI: CIP in RS & OSN" when reaching the step of selecting article type name in submission process.


Guest Editors

Dr. Irfan Mehmood (Leading Guest Editor)

Assistant Professor, Sejong University, Seoul, Republic of Korea

Email: [email protected][email protected]

Profile: https://scholar.google.com.pk/citations?user=9EuBM9UAAAAJ&hl=en


Dr. Zhihan Lv

Research Associate, University College London, UK

Email: [email protected][email protected]

Profile: http://lvzhihan.github.io/


Dr. Yudong Zhang

Professor, Nanjing Normal University, China and Columbia University, USA

Email: [email protected]

Profile: http://schools.njnu.edu.cn/computer/person/yudong-zhang


Dr. Zheng Yan

Xidian University, China & Aalto University, Finland

Email: [email protected]

Profile: http://web.xidian.edu.cn/yanzheng/en/index.html


Dr. Mehmet A. Orgun

Full Professor, Macquarie University, Sydney, Australia

Email: [email protected]

Profile: https://scholar.google.co.kr/citations?user=FpZlwKUAAAAJ&hl=en&oi=sra


Dr. Mario Vento

Full Professor, University of Salerno, Italy

Email: [email protected]

Profile: https://scholar.google.co.kr/citations?user=3PwXGpgAAAAJ&hl=en

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