CFP: TKDE Special Issue on Online Recommendation Using AI and Big Data Techniques

Call for Papers
Special Issue: Online Recommendation Using AI and Big Data Techniques
IEEE Transactions on Knowledge and Data Engineering

Aims and Scope

The rapid growth of online service platforms has significantly influenced the way users conduct daily activities. In response to the requirements of frequent online activities on hugh information, recommendation has become one of the best ways for the organizations, governments, and individuals to understand their users and promote their products or services. Effective recommendation of online items and online consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. However, there are still many research challenges in this area, such as the discovery of contexts, the sequential user behavior influence, the explainability of online system, the user interaction of system, the big data management of online services etc. Especially, the highly dynamic network data on online platforms make these challenges even critical. This special section focuses on the new recommendation solutions using AI and Big Data techniques. We would like to invite authors to submit papers on all aspects of online recommendation techniques. 

The list of possible topics includes, but is not limited to:
	Applications of recommendation systems
	Context discovery in recommendation
	Result summarization, explanation, and presentation in recommendation;
	Trust of recommendation results
	User intent and dialog state tracking in recommendation
	User models and user behavior analysis in the context of recommendation 
	Advanced data mining, machine learning techniques for recommendation;
	Big data analytics technique and its applications to recommendation;
	Context-aware recommendation;
	Conversational recommendation;
	Scenario-Oriented Recommendation;
	Surveys, reviews and prospects on recommendation techniques.

Guest Editors:

Authors can submit their manuscripts via the Manuscript Tracking System at https://mc.manuscriptcentral.com/tkde-cs. Reviewing will be single-blind. We will follow policies for plagiarism, submission confidentiality, reviewer anonymity, prior and concurrent paper submission based on the Publisher of TKDE.

Prof. Lei Chen, Hong Kong University of Science & Technology, Hong Kong
Dr. Xiangmin Zhou, RMIT University, Australia
Prof. Xiaochun Yang, Northeastern University, China
Prof. Timos Sellis, Swinburne University of Technology, Australia

Contact
For questions or more information, please contact the guest editors Lei Chen (leichen@cse.ust.hk), Xiangmin Zhou(xiangmin.zhou@rmit.edu.au), Xiaochun Yang (yangxc@mail.neu.edu.cn) and Timos Sellis (timossellisg@gmail.com)

Schedule
Manuscript Submission Due:  1st July, 2021
First Round of Reviews Completed: 1st September, 2021
Revision Due: 1st November, 2021 (60 days after receiving the notification letter)
Final Decision: 1st January, 2022
Final Manuscript Due: 31st January 2022
Expected Publication Date: Early 2022