As the world is growing technologically, the accumulation of data widens the scope for newer opportunities in different fields. In that manner, the future relies on emerging technologies like big data analytics, artificial intelligence (AI), the Internet of Things (IoT), machine learning, data mining, and deep learning. Specifically, the combination of technologies like machine learning and AI could support the transformation of massive datasets into insightful intelligent information. These meaningful insights could help in improving services, understanding the collection of complex data, optimization of processes, enhancing better decision-making ability, and much more. Also, this smart architecture focuses on resolving issues and developing alternative knowledge along with the analytical ability to predict the future. The capabilities of this data-driven intelligence are achieved by comprehending diagnostic, descriptive, prescriptive, predictive, and decisive datasets. On the other hand, it is equally important to turn our focus on technologies like distributed computing that have the utmost relevance in this era of decentralization. The network of multiple software components could provide benefits over a centralized system (like scalability and redundancy) even though the systems are physically separated. The integration of these smart tools could lend its contributions to various fields like finance, e-commerce, healthcare, manufacturing, and education. Consequently, offering advancements in these technologies could enhance the futuristic growth of the e-commerce platform.
Since recommender systems filter information according to preferences, the effectiveness of the e-business turns out to be highly recognizable. Likely, the amalgamation of the aforesaid technologies with this recommendation algorithm can profoundly deliver a robust, persistent, and trustworthy environment for the emerging e-commerce industry. Despite having so many beneficial roles, the presence of some disadvantages could subsequently retard the performance of the system. Commencing from the inability to compete with changing human behavior, absence of creativity, high rate of technological bias, errors in the selection of algorithms, inadequate data acquisition methods, etc., are a few technical gaps that are present in this framework. Changing these challenging factors into opportunities could elevate this system for the future.
With this aim, researchers, academicians, and other professionals related to this field are welcomed to bring in fruitful developments with their innovative works. Thus, this special section encourages researchers from different domains to submit their new solutions for substantiating a sustainable e-commerce environment. Topics of interest include, but are not limited to, the following:
- IoT-driven recommender systems for e-commerce using big data intelligence and distributed computing
- Multi-modal mining and big data intelligence for personalization in e-commerce services
- Big data intelligence-empowered collaborative filtering for e-commerce applications
- Consumer behavior mining with big data intelligence for supply chain and inventory management
- Contextual recommender systems based on big data intelligence for personalized e-commerce
- Intelligent performance analyzer with big data and data mining for the emerging e-commerce platform
- Ambient intelligence in e-business using evolving IoT, big data analytics, and cloud architectures
- Optimization of e-commerce services using cloud-based big data intelligence and distributed computing
- Ubiquitous computing in data-driven recommender systems for enterprise e-business transformation
- Role of big data intelligence, IoT, and data mining for smart computing in the e-commerce environment
Important Dates
Article Submission Deadline: 21 May 2022
Authors Notification Date: 5 August 2022
Revised Papers Due: 25 October 2022
Final Notification Date: 30 December 2022
Submission Guidelines
For author information and guidelines on submission criteria, please visit the OJ-CS Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Questions?
Contact the lead guest editor at gmanogaran@ieee.org.
Guest Editors:
- Gunasekaran Manogaran (lead guest editor), Howard University, USA
- Ching-Hsien Hsu, Asia University, Taiwan
- Mamoun Alazab, Charles Darwin University, Australia
- Syed Hassan Ahmed, JMA Wireless, USA
- Oscar Sanjuán Martínez, Universidad Internacional de la Rioja (UNIR), Logroño, Spain
