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Customer Profiling Using Internet of Things Based Recommendations

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shili Mohamed & Kaouthar Sethom & Abdallah Namoun & Ali Tufail & Ki-Hyung Kim & Hani Almoamari, 2022. "Customer Profiling Using Internet of Things Based Recommendations," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11200-:d:909034
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    References listed on IDEAS

    as
    1. Juan-Pedro Cabrera-Sánchez & Iviane Ramos-de-Luna & Elena Carvajal-Trujillo & Ángel F. Villarejo-Ramos, 2020. "Online Recommendation Systems: Factors Influencing Use in E-Commerce," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    2. Jaekyeong Kim & Ilyoung Choi & Qinglong Li, 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    3. Yan Guo & Chengxin Yin & Mingfu Li & Xiaoting Ren & Ping Liu, 2018. "Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business," Sustainability, MDPI, vol. 10(1), pages 1-13, January.
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