Customer Profiling Using Internet of Things Based Recommendations

Shili Mohamed (), Kaouthar Sethom, Abdallah Namoun, Ali Tufail, Ki-Hyung Kim and Hani Almoamari
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Shili Mohamed: Innov’COM Laboratory, National Engineering School of Carthage, University of Carthage, Ariana 2083, Tunisia
Kaouthar Sethom: Innov’COM Laboratory, National Engineering School of Carthage, University of Carthage, Ariana 2083, Tunisia
Abdallah Namoun: Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Ali Tufail: School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Bandar Seri Begawan BE1410, Brunei
Ki-Hyung Kim: Department of Cyber Security, Ajou University, Suwon 16499, Korea
Hani Almoamari: Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia

Sustainability, 2022, vol. 14, issue 18, 1-21

Abstract: The digital revolution caused major changes in the world because not only are people increasingly connected, but companies are also turning more to the use of intelligent systems. The large amount of information about each product provided by the e-commerce websites may confuse the customers in their choices. The recommendations system and Internet of Things (IoT) are being used by an increasing number of e-commerce websites to help customers find products that fit their profile and to purchase what they had already chosen. This paper proposes a novel IoT based system that would serve as the foundation for creating a profile, which will store all the contextual data, personalize the content, and create a personal profile for each user. In addition, customer segmentation is used to determine which items the client wants. Next, statistical analysis is performed on the extracted data, where feelings, state of mind, and categorization play a critical role in forecasting what customers think about products, services, and so on. We will assess the accuracy of the forecasts to identify the most appropriate products based on the multi-source data thanks to the IoT, which assigns a digital footprint linking customers, processes, and things through identity-based information and recommendations, which is applied by using Raspberry Pi and other sensors such as the camera. Moreover, we perform experiments on the recommendation system to gauge the precision in predictions and recommendations.

Keywords: product recommendation; e-commerce; internet of things; YOLOv5; deep sort (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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