Advertisement

The commodity recommendation method for online shopping based on data mining

  • Chunhua Ju
  • Jie Wang
  • Guanglan Zhou
Article
  • 28 Downloads

Abstract

With the development of E-commerce, more and more people have strong desire to buy goods on the online shopping platform. But they often need to spend more time searching for satisfactory goods because of the large amount of data. We propose a commodity recommendation model of online shopping based on data mining method in this paper. At first, we calculate the similarity between online shopping data of users’ behavior record and commodity rate, and identify the user with highest similarity as the friend of target user. Then we utilize the data of browsing history and collection commodity to analyze the recent demands of target user, and produce a demand list from the target user. After that, we search for specific commodities in friend’s shopping record according to the target user demand list, and make recommendation for the target user. Taking Taobao as the research object, we conclude that the proposed method is more accurate, and the accuracy value of our methods reached 0.315 at the condition of P@N equalling to 15 from the experiment results.

Keywords

Data mining Online shopping Commodity recommendation Similarity Requirement analysis 

Notes

Acknowledgments

The authors thank Dr. Wanqiong Tao who participated in writing or editing of the manuscript. This paper is supported by Zhejiang Province Public Welfare Technology Application Research Project (LGN18G010001), China Postdoctoral Fund (2018 M632497), Zhejiang Postdoctoral Fund (2017-117), Key Project of Social Science Fund (No. 16ZDA053), Philosophy and Social Science Foundation of Zhejiang Province (17YJA630125, 17NDJC107YB, 16NDJC189YB), Natural Science Foundation of China (No. 71203196, 71401156, 71671165, 71702164), Zhijiang Scholar Program (G665), Zhejiang First class discipline A- Management Science, Zhejiang Gongshang University Postdoctoral Fund. The authors thank them heartedly for supporting the paper funds.

References

  1. 1.
    Chen X, Ge X, Ma G (2016) Network shopping user behavior analysis based on data mining[J]. Journal of Mudanjiang Normal University (Natural Sciences Edition) 9401:32–35Google Scholar
  2. 2.
    Hasan B (2016) Perceived irritation in online shopping: the impact of website design characteristics[J]. Comput Hum Behav 54:224–230CrossRefGoogle Scholar
  3. 3.
    Jhamb Y, Fang Y (2017) A dual-perspective latent factor model for group-aware social event recommendation. Inf Process Manag 53(3):559–576CrossRefGoogle Scholar
  4. 4.
    Jiang M, Cui P, Wang F, Zhu W, Yang S (2014) Scalable recommendation with social contextual information. IEEE Trans Knowl Data Eng 26(11):2789–2802CrossRefGoogle Scholar
  5. 5.
    Li H, Ma X-P, Shi J (2016) Incorporating trust relation with PMF to enhance social network recommendation performance. Int J Pattern Recognit Artif Intell 30(6):1–13CrossRefGoogle Scholar
  6. 6.
    Lim YJ, Osman A, Salahuddin SN, Romle AR, Abdullah S (2016) Factors influencing online shopping behavior: the mediating role of purchase intention[J]. Procedia Economics and Finance 35:401–410CrossRefGoogle Scholar
  7. 7.
    Lv X-l, Wu X-z (2007) Electronic commerce customer web shopping behavior mining [J]. Statistics & Information Forum 22(3):29–32Google Scholar
  8. 8.
    Malik G, Guptha A (2013) An empirical study on behavioral intent of consumers in online shopping[J]. Business Perspectives & Research 2:13–26CrossRefGoogle Scholar
  9. 9.
    Moe WW, Fader PS (2004) Dynamic conversion behavior at E-commerce sites[J]. Manag Sci 50(3):326–335CrossRefGoogle Scholar
  10. 10.
    Morganosky MA, Cude BJ (2000) Consumer response to online grocery shopping[J]. Int J Retail Distrib Manag 28(1):17–26CrossRefGoogle Scholar
  11. 11.
    Sismeiro C, Bucklin RE (2004) Modeling purchase behavior at an E-commerce web site: a task-completion approach[J]. J Mark Res 5(2):306–323CrossRefGoogle Scholar
  12. 12.
    Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, Yan H (2015) Recommender systems based on social networks. J Syst Softw 99:109–119CrossRefGoogle Scholar
  13. 13.
    The quantity of Internet users reached up to a 772 million, the penetration rate of Internet is 55.5% [EB/OL]. http://www.chyxx.com/industry/201712/589156.html. 2017-12-04/2018-03-26
  14. 14.
    The size of China's online shopping users reached 506 million as of December 2017. [EB/OL]. http://www.cac.gov.cn/2018-01/31/c_1122346138.htm. 2018-01-31/2018-5-11
  15. 15.
    Xu C (2018) A novel recommendation method based on social network using matrix factorization technique. Inf Process Manag 54(3):463–474CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Management ScienceZhejiang Gongshang UniversityHangzhouChina
  2. 2.School of Business AdministrationZhejiang Gongshang UniversityHangzhouChina
  3. 3.School of Statistics, Modern Business Research CenterZhejiang Gongshang UniversityHangzhouChina

Personalised recommendations