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

in Elsevier "Journal of Computational Science", IF: 1.748 (SCI/SCIE).

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:

Computational intelligence solutions for OSNs and recommendation in recommender systems
Computational intelligence in mobile-cloud based computing for social network recommendation services
Big data analytics for community activity prediction, management, and decision-making in OSNs
Fuzzy system theory in OSNs and recommender systems
Social data analytical approaches using computational methods
Deep learning and machine learning algorithms for efficient indexing and retrieval in multimedia recommendation systems and OSNs
Intelligent techniques for smart surveillance and security in OSNs
Modeling, data mining, and public opinion analysis based on social big data
Crowd computing-assisted access control and digital rights management for OSNs
Evolutionary algorithms for data analysis and recommendations
Crowd intelligence and computing paradigms for sentimental analysis and recommendation
Applied soft computing for content security, vulnerability and forensics in OSNs
Computational intelligence in multimedia computing and context-aware recommendation
Scalable, incremental learning and understanding of OSN big data with its real-world applications for visualization, HCI, and virtual reality community
Crowd intelligence-assisted ubiquitous, personal, and mobile social media applications
Recommender systems for crowdsourcing and privacy preserving crowdsourcing
Crowdsourcing and crowd sensing based on OSN and its applications for trust evaluation
Artificial intelligence and pattern recognition technologies for recommendation in healthcare
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: irfan@sejong.ac.kr, irfanmehmood@ieee.org
Profile: https://scholar.google.com.pk/citations?user=9EuBM9UAAAAJ&hl=en

Dr. Zhihan Lv
Research Associate, University College London, UK
Email: lvzhihan@gmail.com, z.lu@ucl.ac.uk
Profile: http://lvzhihan.github.io/

Dr. Yudong Zhang
Professor, Nanjing Normal University, China and Columbia University, USA
Email: zhangyudong@njnu.edu.cn
Profile: http://schools.njnu.edu.cn/computer/person/yudong-zhang

Dr. Zheng Yan
Xidian University, China & Aalto University, Finland
Email: zhengyan.pz@gmail.com
Profile: http://web.xidian.edu.cn/yanzheng/en/index.html

Dr. Mehmet A. Orgun
Full Professor, Macquarie University, Sydney, Australia
Email: mehmet.orgun@mq.edu.au
Profile: https://scholar.google.co.kr/citations?user=FpZlwKUAAAAJ&hl=en&oi=sra

Dr. Mario Vento
Full Professor, University of Salerno, Italy
Email: mvento@unisa.it
Profile: https://scholar.google.co.kr/citations?user=3PwXGpgAAAAJ&hl=en