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


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.

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The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research Group No. RG-75.

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Correspondence to Jameel Khader.

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Shamshoddin, S., Khader, J. & Gani, S. Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques. Electron Commer Res 20, 241–258 (2020).

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  • Prediction
  • IoT
  • Deep learning with collaborative filtering
  • Recommendations
  • Social Networks