IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p3036-d343698.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/3036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/7/3036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Gupta, Mukul & Kumar, Pradeep, 2020. "Recommendation generation using personalized weight of meta-paths in heterogeneous information networks," European Journal of Operational Research, Elsevier, vol. 284(2), pages 660-674.
    3. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    4. T. Venkatesan & K. Saravanan & T. Ramkumar, 2019. "A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-21, March.
    5. K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.
    6. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2021. "Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 389-409, June.
    7. Xiong, Yingqiu & Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Chai, Yidong & Ling, Haifeng, 2024. "Review-based recommendation under preference uncertainty: An asymmetric deep learning framework," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1044-1057.
    8. Hana Kim & Daeho Lee & Min Ho Ryu, 2018. "An Optimal Strategic Business Model for Small Businesses Using Online Platforms," Sustainability, MDPI, vol. 10(3), pages 1-11, February.
    9. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    10. Behera, Rajat Kumar & Gunasekaran, Angappa & Gupta, Shivam & Kamboj, Shampy & Bala, Pradip Kumar, 2020. "Personalized digital marketing recommender engine," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    11. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
    12. 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.
    13. 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.
    14. 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.
    15. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    16. Li, Man & Wen, Luosheng & Chen, Feiyu, 2021. "A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    17. 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.
    18. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    19. Jun Yao & Jianhui Chen, 2023. "A Study on the Characteristics of Middle-aged Chinese Female Users Based on Clothing Needs," Asian Social Science, Canadian Center of Science and Education, vol. 19(4), pages 1-86, August.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3036-:d:343698. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.