IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2022y2022i1n7414419.html

Collocating Recommendation Method for E‐Commerce Based on Fuzzy C‐Means Clustering Algorithm

Author

Listed:
  • LiJia Wang
  • Yanyan Jiang

Abstract

In order to reduce the time for customers to select commodities they are interested in, improve the purchase efficiency, improve the success rate of sales of merchants, and create greater economic benefits for enterprises and merchants, this project collects information and data of e‐commerce users, using neural network model to analyze and mine data characteristics and shopping records of e‐commerce users. According to the analysis results, a user commodity recommendation system based on e‐commerce is implemented by using data mining technology. Through the combination of database technology, the transaction and browsing data generated in the process of e‐commerce transactions are collected. The collected data is preformatted and used as the input of data mining. Then, it uses data mining technology to mine and analyze the commodities that users are interested in, makes matching according to the types of commodities, and recommends the commodities that users are interested in under a given scene according to the established prediction model. By combining fuzzy clustering with collaborative filtering algorithm, this paper recommends the products that users are interested in, which are mined from historical data and commodity information.

Suggested Citation

  • LiJia Wang & Yanyan Jiang, 2022. "Collocating Recommendation Method for E‐Commerce Based on Fuzzy C‐Means Clustering Algorithm," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:7414419
    DOI: 10.1155/2022/7414419
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/7414419
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/7414419?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Png, Ethan & Srinivasan, Seshadhri & Bekiroglu, Korkut & Chaoyang, Jiang & Su, Rong & Poolla, Kameshwar, 2019. "An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings," Applied Energy, Elsevier, vol. 239(C), pages 408-424.
    2. Evan Fields & Carolina Osorio & Tianli Zhou, 2021. "A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand," Transportation Science, INFORMS, vol. 55(3), pages 616-636, May.
    3. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    4. Sebastian Schroff & Stephan Meyer & Hans-Peter Burghof, 2016. "Retail investor information demand – speculating and investing in structured products," The European Journal of Finance, Taylor & Francis Journals, vol. 22(11), pages 1063-1085, September.
    Full references (including those not matched with items on IDEAS)

    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. Yaqin Guang & Shunyong Li & Quanping Li, 2022. "Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    2. Mingfeng Tang & Mei Mei & Cuiwen Li & Xingyang Lv & Xushuang Li & Lihao Wang, 2020. "How does an individual’s default behavior on an online peer-to-peer lending platform influence an observer’s default intention?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-20, December.
    3. Wolfgang Pointner & Burkhard Raunig, 2018. "A primer on peer-to-peer lending: immediate financial intermediation in practice," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/18, pages 36-51.
    4. Zhou Rongxi & Xiong Yahui & Wang Ning & Wang Xizu, 2019. "Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis," Journal of Systems Science and Information, De Gruyter, vol. 7(5), pages 399-421, October.
    5. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    6. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    7. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    8. Chen Xiao & Yaling Chen, 2025. "Risk Control of Online Lending Platform Under Regulatory Penalty," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2025(1).
    9. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    10. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
    11. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    12. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    13. Wu, Manhua & Tian, Xiujuan & Ma, Lin & Peng, Nianjiao, 2024. "Role transition, investment practice and risk Management of Peer-to-peer Lending Investors: Based on the perspective of investor learning," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    14. Tomáš Plíhal & Oleg Deev, 2026. "P2P loan performance forecasting and portfolio optimization: the role of distance metrics in mixed data classification," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 40(1), pages 97-133, March.
    15. Käfer Benjamin, 2018. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," Review of Economics, De Gruyter, vol. 69(1), pages 1-25, April.
    16. Dorfleitner, Gregor & Rad, Jacqueline & Weber, Martina, 2017. "Pricing in the online invoice trading market: First empirical evidence," Economics Letters, Elsevier, vol. 161(C), pages 56-61.
    17. Xi Yang & Wenjuan Fan & Shanlin Yang, 2020. "Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    18. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    19. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    20. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.

    More about this item

    Statistics

    Access and download statistics

    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:wly:jjmath:v:2022:y:2022:i:1:n:7414419. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.