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Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Author

Listed:
  • Yan Guo

    (College of Management Science, Chengdu University of Technology, Chengdu 610059, China)

  • Chengxin Yin

    (College of Architecture Engineering, Chengdu Aeronautic Polytechnic, Chengdu 610100, China)

  • Mingfu Li

    (College of Architecture Engineering, Chengdu Aeronautic Polytechnic, Chengdu 610100, China)

  • Xiaoting Ren

    (College of Public Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Ping Liu

    (Department of Economic and Management, Sichuan Technology Business College, Chengdu 611830, China)

Abstract

A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:147-:d:126094
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    References listed on IDEAS

    as
    1. Scholz, Michael & Dorner, Verena & Franz, Markus & Hinz, Oliver, 2015. "Measuring Consumers' Willingness-to-Pay with Utility-based Recommendation Systems Decision Support Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77134, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. 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.
    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. Mingwei Sun & Katarzyna Grondys & Nazim Hajiyev & Pavel Zhukov, 2021. "Improving the E-Commerce Business Model in a Sustainable Environment," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
    4. Raheleh Hassannia & Ali Vatankhah Barenji & Zhi Li & Habib Alipour, 2019. "Web-Based Recommendation System for Smart Tourism: Multiagent Technology," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    5. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    6. Hyunwoo Hwangbo & Yangsok Kim, 2019. "Session-Based Recommender System for Sustainable Digital Marketing," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    7. Liang Xiao & Qibei Lu & Feipeng Guo, 2020. "Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    8. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    9. Jaeho Jeong & Dongeon Kim & Xinzhe Li & Qinglong Li & Ilyoung Choi & Jaekyeong Kim, 2022. "An Empirical Investigation of Personalized Recommendation and Reward Effect on Customer Behavior: A Stimulus–Organism–Response (SOR) Model Perspective," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    10. Chunsheng Cui & Jielu Li & Zhenchun Zang, 2021. "Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation," Mathematics, MDPI, vol. 9(21), pages 1-13, October.
    11. Paulo Rita & Ricardo F. Ramos, 2022. "Global Research Trends in Consumer Behavior and Sustainability in E-Commerce: A Bibliometric Analysis of the Knowledge Structure," Sustainability, MDPI, vol. 14(15), pages 1-20, August.
    12. Liang Xiao & Hangxiao Mao & Shu Wang, 2020. "Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce," Sustainability, MDPI, vol. 12(6), pages 1-25, March.

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