Big Data and Machine Learning in Electronic Commerce

Dear Colleagues,

In recent years, the development of e-commerce has gradually increased throughout the world. Also referred to as electronic commerce, e-commerce shapes the way people shop for products in ways that improve the market. The Gulf Cooperation Council (GCC) continuously monitors global markets and characterizes the business process according to people's interests. GCC countries are critical players in e-commerce because they are expected to reach an e-commerce market of up to $20 million by the year 2020. To achieve this goal, research of several types has been performed to enhance the purchase rate of goods and services via the online channel. Analysis has shown that the e-commerce platform is more convenient and has more product selection choices. This e-commerce advancement does not mean that all e-commerce companies are making profits. There are many challenges to e-commerce, such as the absence of online identify verification, delivering worse customer experience, failing to analyze competitors, being stuck in the old ways of selling products, shopping cart abandonment, difficulties in customer loyalty management, struggles to compete on price and shipping, and data security problems. These problems occur due to the lack of business, product, and service information. To overcome the difficulties, big data techniques are utilized to maximize e-commerce.

Big data consists in the variety and volume of data that include business process details, product information, customer interest, and request-related details. The data are collected according to user survey, competitor choice, sales criteria, and reviews, which help to identify customer’s exact requirements. The collected details are processed by applying machine learning techniques to overcome the aforementioned e-commerce challenges. Machine learning techniques can effectively predict future market trends, purchasing criteria, and competitor opinion. Moreover, big data and machine learning techniques analyze the e-commerce data using an effective learning process affordably and flexibly. Therefore, an increasing number of researchers are interested in investigating e-commerce data to provide guidelines for improving the overall business process. Consequently, this Special Issue focuses on big-data- and machine-learning-based e-commerce that will offer an effective platform to develop a better solution for new e-commerce consumers.

The topics of interest for the Special Issue include, but are not limited to, the following:

• E-product recommendation using intelligent machine learning techniques
• Customer data analysis and data security using big data and AI approaches
• Creating customer support systems in e-commerce platforms
• Analyzing the inventory forecasting system using optimized learning approaches
• Big data analytics for prediction and applications in e-commerce
• Analyzing sustainability issues using big data analytic tools
• Big data analytic tools for financial inclusion
• Creating recommendation systems by analyzing customer reviews.
• Logistic management process in e-commerce using optimized machine learning techniques
• Active customer identification in e-commerce

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Theoretical and Applied Electronic Commerce Research is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• e-commerce
• big data
• machine learning
• recommendation
• inventory forecasting
• financial inclusion
• customer reviews
• active customer