Special Issue "Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce"

Submission Deadline: 31 October 2020 (closed)
Guest Editors
Prof. Honghao Gao, Shanghai University, China
Prof. Dr. Jung Yoon Kim, Gachon University, South Korea
Prof. Yuyu Yin, Hangzhou Dianzi University, China

Summary

In recent years, there is an increasing interest in new business models and strategies from both practitioners and researchers. The significant market transformation has been accomplished by leading E-commerce vendors such as Alibaba and Amazon through their innovative and highly scalable E-Commerce eco-systems. In the context of E-Commerce eco-system, there are hundreds of millions of consumers, thousands of businesses and shops, and hundreds of delivery people. Alibaba Group, as one of the main E-Commerce providers, cooperates with tens of thousands of software vendors to provide all necessary software services to support the business. With the booming of eco-business, more ecological roles in E-Commerce businesses emerge. For instance, Alibaba Group has expanded its business scale from Taobao Software to several business units, with 10000 plus technical staff. Large E-Commerce businesses such as Alibaba Group need to support a large number of applications and business modules, and cater for hundreds of business requirements and independent changes on a daily basis. Another new commercial mode, the sharing economy, such as Airbnb, Instacart, and Uber, has already operated on the basis of E-commerce eco-systems and information technologies. The intersection of these information technologies and business models provides ample research opportunities in intelligent processing of data, information and knowledge.


Keywords
• Best commercial practices and case studies in the field of E-commerce;
• Knowledge management for E-commerce;
• Date management for E-commerce;
• Literature review on emerging issues of new business models and strategies;
• New information technology for E-commerce, AI technology, Blockchain, Edge computing, service computing, etc;
• Business process management for E-commerce;
• Big data management and application in E-commerce;
• Demand analysis in E-commerce companies;
• Implementation risk and benefit of adopting digital technologies in E-commerce;
• Innovative mode in E-commerce;
• Consumer information sharing in E-commerce;
• The use of internet platform in E-commerce;
• Empirical studies of business practices and performance.

Published Papers


  • Number Entities Recognition in Multiple Rounds of Dialogue Systems
  • Abstract As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named as number entity. The discontinuous… More
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  • Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning
  • Abstract With the continuous development of artificial intelligence technology, its application field has gradually expanded. To further apply the deep reinforcement learning technology to the field of dynamic pricing, we build an intelligent dynamic pricing system, introduce the reinforcement learning technology related to dynamic pricing, and introduce existing research on the number of suppliers (single supplier and multiple suppliers), environmental models, and selection algorithms. A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions. The first step is to analyze the pricing strategies of e-commerce… More
  •   Views:613       Downloads:486        Download PDF

  • A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering
  • Abstract User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the fuzzy C-means clustering algorithm (FCM),… More
  •   Views:598       Downloads:490        Download PDF