Impact Score 2.31
Dr. ChuanRen Liu
Department of Business Analytics and Statistics
University of Tennessee
Dr. Xin Ye
School of Economics and Management
Dalian University of Technology
Data-intensive methodologies and tools have been playing a pivotal role for various research and application areas with contributions from diverse communities including computer scientists, statisticians, mathematicians, and industrial practitioners. While most data-intensive methodologies and tools are developed for general problem settings, there are often unique challenges in developing effective and efficient solutions for a specific domain or a novel real-world application. Due to the need of incorporating domain knowledge in the data modeling and analytical process and the challenge to discover actionable knowledge hidden in complex data, data-intensive research in specialized domains can be more challenging and require more manual intervention.
Such challenges are faced especially in the fast-changing e-commerce research and applications, such as e-commerce knowledge engine, product search and recommendation, online advertising, customer targeting, online-offline integration, inventory optimization, intelligent customer service, and fraud detection. Indeed, the unique challenges in e-commerce problems have attracted great attention in data science research, such as data mining, machine learning, and artificial intelligence.
To foster further advances of data-intensive research in e-commerce, this special issue aims to share the open challenges, learned lessons, and best practices in developing and applying data-driven solutions for problems from e-commerce and related applications. We hope this special issue will benefit interdisciplinary data science research in not only e-commerce but also related domains such as information systems and technology, marketing, finance and supply chain management, with methodologies involving network analytics, text mining, sequential pattern mining, predictive modeling, and reinforcement learning.
The topics of interest include but not limited to:
We also invite authors to submit a short paper or extended abstract to the International Workshop on Domain-Driven Data Mining. The workshop is organized with SDM 2021, a virtual conference, April 29-May 1, 2021. Domain-driven data mining, where e-commerce is a major application environment, is an important research area in SDM given the highly applied and interdisciplinary nature of the conference. Authors can present preliminary results at the workshop before submitting to the special issue. This will help us to identify reviewers and accelerate the review process for the special issue. Submission to the workshop is optional.
Biography of Guest Editors
Chuanren Liu is an Assistant Professor in the Business Analytics and Statistics Department at the University of Tennessee, Knoxville. His research interests include data mining and knowledge discovery, and their applications in business analytics. He has published papers in refereed journals and conference proceedings, such as IEEE Transactions on Data and Knowledge Engineering, INFORMS Journal on Computing, European Journal of Operational Research, Annals of Operations Research, IEEE Transactions on Cybernetics, Knowledge and Information Systems and SIGKDD, ICDM, SDM, AAAI, IJCAI, UbiComp, IEEE BigData, etc. He has served on the Senior Program Committee for AAAI and the editorial board for Electronic Commerce Research and Applications.
Xin Ye is a Professor at the School of Economics and Management, Dalian University of Technology, Dalian, China. He received the Ph.D. degree from the Dalian University of Technology (DUT), Dalian, China, in 2006. His research interests include artificial intelligence, big data, cloud computing, emergency management, and information systems. He has published and edited two textbooks and more than 60 articles in journals such as Knowledge-Based Systems, Decision Support Systems, Information Sciences, Data and Knowledge Engineering, Neural Processing Letters, and Electronic Commerce Research and Applications.