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Academic Journal of Business & Management, 2019, 1(1); doi: 10.25236/AJBM.190111.

Precision Recommendation Algorithm for E-commerce Service:Taking Online Tutoring as an Example


Jiang Tian1,  Xue Wang2,  Wei Li3,  Futao Tian4

Corresponding Author:
Jiang Tian

1. School of Economics and Managemen, University of Electronic Science and Technology of China, Chengdu 610054, China
2. School of Information and Communication Engineering,University of Electronic Science and Technology of China, Chengdu 611731, China
3. Enterprise Culture Department, PetroChina Southwest Oil and Gas field Company, Chengdu  610051, China
4. School of Management, University College London, WC1E 6BT, London, UK


With continuous improvement of individualized demand for consumers, traditional e-commerce has been unable to meet the growing demand for a better life. E-commerce service presents the intelligent development tendency in order to provide accurate and personalized service. Recommendation system for e-commerce provides customers with the most suitable product or service based on precise recommendation for customer’s demand and preference. This paper first establishes user portrait and service portrait to achieve precision matching for both parties supply and demand in e-commerce, and further analyzes the precise recommendation service by taking tutoring service as an example to accurately meet the demand of students for tutoring service.


Precision matching, recommendation service, algorithm, tutoring service

Cite This Paper

Jiang Tian,  Xue Wang,  Wei Li,  Futao Tian. Precision Recommendation Algorithm for E-commerce Service:Taking Online Tutoring as an Example. Academic Journal of Business & Management (2019) Vol. 1: 71-79. https://doi.org/10.25236/AJBM.190111.


[1] Li H.L.(2016).Precision marketing under the background of big data.  Intelligence,2016(07):289-290.
[2] Charu C. Aggarwal. Principle and Practice of Recommendation System[M].Beijing:Mechanical Industry Press,2018.
[3] Xiang L. (2012). Practices of Recommendation System[M]. Beijing:Post & Telecom Press,2012.
[4] Li C. Research on Bottleneck of Electronic Commerce Recommendation System[M]. Beijing:Science Press,2016.
[5] Li C. Research on bottleneck of cooperative filtering in electronic commerce recommendation system [D]. Hefei:Hefei University of Technology,2009.
[6] Cui C. & Luo O. (2018). The realization thoughts on of user portraits in scientific and research knowledge community. Information and Communication Technologies and Policies, 2018(06):75-78.
[7] Yang B. (2013). Application research of cooperative filtering algorithm in mobile electronic commerce recommendation system[D]. Xiamen:Xiamen University,2013.
[8] Zhang D. (2014). Research and application of individualized recommendation algorithm[D]. Zhejiang:Zhenjiang University,2014.