IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01059-1.html
   My bibliography  Save this article

Using cloud computing platform of 6G IoT in e-commerce personalized recommendation

Author

Listed:
  • Junhai Wang

    (Zhejiang Technical Institute of Economics)

  • Yiman Zhang

    (Wenzhou University)

Abstract

In order to improve the exposure of commodities in e-commerce, accurately recommend personalized commodities and stimulate users' consumption of commodities, on the basis of a lot of literature, a personalized recommendation framework for e-commerce is built based on the open-source Hadoop cloud computing platform. According to the similarity of the algorithm, through the comparison of the project collaborative filtering algorithm based on cloud computing, user collaborative algorithm and the improved algorithm based on matrix filling and time context, the optimal algorithm is obtained and evaluated comprehensively in two aspects of algorithm performance and personalized recommendation performance. The results show that compared with UBCF (User Based Collaborative Filtering) and IBCF (Item Based Collaborative Filtering), the IA (Improved algorithm) based on matrix filling and time context is more accurate, its MAE (Mean absolute error) is smaller, and the scalability is enhanced. With the increase of nodes, the operation efficiency of the algorithm is obviously improved. The data recommendation effect of IA algorithm is better than that of IBCF and UBCF, and the commodities recommended to consumers are more in line with their preferences, which can improve the novelty and coverage rate of the algorithm, improve the exposure of the commodities in all aspects, and greatly promote the consumer behavior of users. The data mining system based on Hadoop cloud computing is introduced into e-commerce, which can provide theoretical basis for related research, and it is also of great significance to social development and academic research.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01059-1
    DOI: 10.1007/s13198-021-01059-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01059-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01059-1?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eunyoung Choi & Kun Chang Lee, 2019. "Effect of Trust in Domain-Specific Information of Safety, Brand Loyalty, and Perceived Value for Cosmetics on Purchase Intentions in Mobile E-Commerce Context," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    2. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    3. 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.
    4. Chao-Hsing Lee, 2020. "Generation Difference of Post-90s in the Context of the Rise of Social Commerce," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(1), pages 1-2.
    5. Huh, Sung-Yoon & Jo, Manseok & Shin, Jungwoo & Yoo, Seung-Hoon, 2019. "Impact of rebate program for energy-efficient household appliances on consumer purchasing decisions: The case of electric rice cookers in South Korea," Energy Policy, Elsevier, vol. 129(C), pages 1394-1403.
    6. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    7. Yingyan Zhao & Yihong Zhou & Wu Deng, 2020. "Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    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. Zhang, Tianyu & Dong, Peiwu & Zeng, Yongchao & Ju, Yanbing, 2022. "Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model," Journal of Business Research, Elsevier, vol. 139(C), pages 90-105.
    2. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    3. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    4. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    5. Ju-Hee Kim & Younggew Kim & Seung-Hoon Yoo, 2021. "Using a choice experiment to explore the public willingness to pay for the impacts of improving energy efficiency of an apartment," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1775-1793, October.
    6. Zha, Donglan & Yang, Guanglei & Wang, Wenzhong & Wang, Qunwei & Zhou, Dequn, 2020. "Appliance energy labels and consumer heterogeneity: A latent class approach based on a discrete choice experiment in China," Energy Economics, Elsevier, vol. 90(C).
    7. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    8. Yam B. Limbu & A. F. M. Jalal Ahamed, 2023. "What Influences Green Cosmetics Purchase Intention and Behavior? A Systematic Review and Future Research Agenda," Sustainability, MDPI, vol. 15(15), pages 1-15, August.
    9. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    10. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    11. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    12. Heesup Han & Hyoungeun Moon & Antonio Ariza-Montes & Soyeun Lee, 2020. "Sensory/Health-Related and Convenience/Process Quality of Airline Meals and Traveler Loyalty," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    13. Shaojun Zhou & Yufei Liu & Yuhan Liu, 2024. "A Market Convergence Prediction Framework Based on a Supply Chain Knowledge Graph," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    14. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 0. "Analyzing the impact of review recency on helpfulness through econometric modeling," 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. 0, pages 1-8.
    15. Agnieszka Zablocki & Bodo Schlegelmilch & Michael J. Houston, 2019. "How valence, volume and variance of online reviews influence brand attitudes," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 61-77, June.
    16. Pham Quang Huy & Vu Kien Phuc, 2023. "Big data in relation with business intelligence capabilities and e-commerce during COVID-19 pandemic in accountant’s perspective," Future Business Journal, Springer, vol. 9(1), pages 1-21, December.
    17. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    18. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    19. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    20. Liu & Li-Lun & Chiang & Chung Hang, 2023. "Patent Trend Analysis: Extended Reality (XR) and Future Virtual Adventure," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 13(2), pages 1-2.

    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:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01059-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.