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Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques

Author

Listed:
  • Sadaf Shamshoddin

    (King Saud University)

  • Jameel Khader

    (King Saud University)

  • Showkat Gani

    (King Saud University)

Abstract

Collaborative filtering plays an important role in predicting consumer preferences in the electronic market. Most of the users purchased the products in the electronic market with the help of the Internet of Things (IoT) and Social Networks. Predicting consumer preference with the consumer’s history is a vital challenge in the recommendation systems. The researchers propose varieties of collaborative filtering techniques, but the accuracy of the results is poor. The main aim of this paper is to propose a deep learning with collaborative filtering technique for the recommendation system to Predicting User preferences from the IoT devices and Social Networks that are beneficial for users based on their preferences in electronic markets. In this paper similarity, neighborhood-based collaborative filtering model (SN-CFM) is introduced. The introduced model recommends the products by predicting consumer preferences based on the similarity of the consumers and neighborhood products. In addition, the introduced deep learning concept gets the information from the previous analysis before making rating to the items. The introduced SN-CFM model compared with other existing recommendation approaches. The results prove that the efficiency of the introduced model.

Suggested Citation

  • Sadaf Shamshoddin & Jameel Khader & Showkat Gani, 2020. "Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques," Electronic Commerce Research, Springer, vol. 20(2), pages 241-258, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09377-0
    DOI: 10.1007/s10660-019-09377-0
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    References listed on IDEAS

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    1. Jiaying Liu & Tao Tang & Xiangjie Kong & Amr Tolba & Zafer AL-Makhadmeh & Feng Xia, 2018. "Understanding the advisor–advisee relationship via scholarly data analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 161-180, July.
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    Cited by:

    1. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.
    2. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.

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