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Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

Author

Listed:
  • Liang Xiao

    (Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Qibei Lu

    (School of Cross-Border E-Commerce, Zhejiang International Studies University, Hangzhou 310023, China)

  • Feipeng Guo

    (Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3036-:d:343698
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    References listed on IDEAS

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    1. Umberto Panniello & Michele Gorgoglione & Alexander Tuzhilin, 2016. "Research Note—In CARSs We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender Systems," Information Systems Research, INFORMS, vol. 27(1), pages 182-196, March.
    2. Geuens, Stijn & Coussement, Kristof & De Bock, Koen W., 2018. "A framework for configuring collaborative filtering-based recommendations derived from purchase data," European Journal of Operational Research, Elsevier, vol. 265(1), pages 208-218.
    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.
    4. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    5. Stijn Geuens & Kristof Coussement & Koen W. de Bock, 2018. "A framework for configuring collaborative filtering-based recommendations derived from purchase data," Post-Print hal-01662029, HAL.
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    Cited by:

    1. Junhai Wang & Yiman Zhang, 2021. "Using cloud computing platform of 6G IoT in e-commerce personalized recommendation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 654-666, August.

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