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Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment

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Abstract

With the development of mobile commerce, situational awareness and Internet of things, the boundaries of e-commerce have been greatly expanded, and it has entered a big data era of business information. However, customers are faced with the problem that information is rich but useful information is hard to get. E-commerce is facing the challenge of how to provide personalized information recommendation services for customers and motivate customers to purchase continuously. Therefore, this paper studies the problem of e-commerce recommendation under the condition of large data, and proposes a scenario-based e-commerce recommendation algorithm based on customer interest. Firstly, according to the characteristics of customer interest such as situational sensitivity and diversity in personalized recommendation, a multi-dimensional customer interest feature vector is established by using distributed cognitive theory to differentiate the sensitive scenarios of customer interest. Then, the collaborative filtering recommendation algorithm is used to realize customer similarity judgment and product recommendation in sensitive scenarios. Experimental results show that the method has good customer interest extraction ability. Compared with other recommendation methods, it has higher recommendation accuracy and can adapt to the high-quality commodity recommendation service in the process of customer continuous purchase under complex circumstances.

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Acknowledgements

This work is supported by the College Scientific Research Project of Inner Mongolia Autonomous Region (NJZY18159), the Science and Technology Innovation Guide Project of Inner Mongolia Autonomous Region (KCBJ2018028), and the Higher Education Teaching Reform Research Project of National Civil Affairs (15085).

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Authors and Affiliations

  1. College of Mechanical Engineering, Inner Mongolia University for the Nationalities, Tongliao, 028000, China

    Xiao-qiang Wu & Lan Wu

  2. School of Mechanical Engineering, Tianjin University of Commerce, Tianjin, 300134, China

    Lei Zhang

  3. School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China

    Xiao-qiang Wu & Song-ling Tian

Authors
  1. Xiao-qiang Wu
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  2. Lei Zhang
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  3. Song-ling Tian
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  4. Lan Wu
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Correspondence to Lei Zhang.

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Wu, Xq., Zhang, L., Tian, Sl. et al. Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment. Electron Commer Res 21, 689–705 (2021). https://doi.org/10.1007/s10660-019-09339-6

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  • DOI: https://doi.org/10.1007/s10660-019-09339-6

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