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Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase

Received: 25 June 2021     Accepted: 16 July 2021     Published: 31 August 2021
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Abstract

Due to the huge amount of data available to buyers, the use of sophisticated algorithms can increase the revenue of ecommerce stores with modern recommender systems. The study was designed to investigate the impact of recommender systems on e-buyers online purchasing behaviors, and predict purchase patterns of buyers. The result of the study revealed how recommender systems affect shopping experience, increase sales for business owners and reach efficient product stocking and delivery. This research proposes an approach of increase in sales and the possibility of purchase prediction based on recommender systems. A survey of e-buyers was taken to determine the impact of recommender systems on past and future purchases. Results show that recommender systems improve shopping experience, increase purchase and can be a good tool to remind buyers of what they need to buy. It shows that recommender systems have the ability to predict what a buyer may be interested in purchasing. Based on the obtained user behavior and e-buyers satisfaction with recommender systems, e commerce stores can take advantage of this to send personalized recommended items to buyers’ emails to increase their sales. As e commerce shopping becomes more accepted globally, findings in this study have benefits to both shopping experience and sales enhancement.

Published in Control Science and Engineering (Volume 5, Issue 2)
DOI 10.11648/j.cse.20210502.11
Page(s) 20-24
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Ecommerce, Artificial Intelligence, Purchase Patterns, Purchase Prediction, Recommender System

References
[1] Dataconomy, An Introduction to Recommendation Engines. Retrieved: 13/07.2021, from http://dataconomy.com/2015/03/ an-introduction-to-recommendation-engines/.
[2] Shaikh, S., Rathi, S., & Janrao, P. Recommendation System in E-Commerce Websites: A Graph Based Approached. 2017 IEEE 7th International Advance Computing Conference (IACC), pp. 931-934, 2017.
[3] Hendrick, D., Lanphear, D., Mahfoud, R., & Megraw, R., U.S. Patent No. US9922360B2. Washington, DC: U.S. Patent and Trademark Office, 2018.
[4] Yeung C. H. (2015). Do recommender systems benefit users https://www.researchgate.net/publication/280221174_Do_recommender_systems_benefit_users Retrieved: 02/03.2021.
[5] Greg Linden, Brent Smith, and Jeremy York • Amazon.com,” Amazon.com Recommendations, Item-to-Item Collaborative Filtering “, JANUARY • FEBRUARY 2003 Published by the IEEE Computer Society 1089-7801/03/$17.00©2003 IEEE INTERNET COMPUTING.
[6] Dr. M J Carmel Mary Belinda, A. S. (2020). A Comprehensive Study of Hybrid Recommendation Systems for E-Commerce Applications. International Journal of Advanced Science and Technology, 29 (3), 4089-4101. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/5163.
[7] Tiwari, S., Richariya, P., Razdan, D., & Tomar, S. (2011). A Web Usage Mining Framework for Business Intelligence. International Conference on Computer and Communication Devices (pp. 342-345). IEEE.
[8] Gartner (2018). Gartner Buyer Experience Summit 2018. Retrieved https://www.gartner.com/en/newsroom/press-releases/2018-02-19-gartner-says-25-percent-of-buyer-service-operations-will-use-virtual-buyer-assistants-by-2020 01/03/2021.
[9] Ref-http://www.business2community.com/strategy/product-recommendation-engines-mean-business-0893268.
[10] E. Turban, D. King, J. Lee and D. Viehland, Electronic Commerce: A Managerial Perspective, Upper Saddle River, NJ, USA: Prentice-Hall, 2002.
[11] F. Ricci, L. Rokach, and B. Shapira, "Introduction to recommender systems handbook," in Recommender systems handbook: Springer, 2011, pp. 1-35.
[12] Y.-M. Li, C.-L. Chou, and L.-F. Lin, “A social recommender mechanism for location-based group commerce,” Information Sciences, vol. 274, pp. 125–142, 2014.
[13] Kim, H. K.; Kim, J. K.; Ryu, Y. U. Personalized Recommendation over a Customer Network for Ubiquitous Shopping. IEEE Trans. Serv. Comput. 2009, 2, 140–151.
[14] Ricci, F.; Rokach, L.; Shapira, B. Introduction to recommender systems handbook. In Recommender Systems Handbook; Springer: Boston, MA, USA, 2011; pp. 1–35.
[15] Kim, H. K.; Ryu, Y. U.; Cho, Y.; Kim, J. K. Customer-driven content recommendation over a network of customers. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2011, 42, 48–56.
[16] Pu, P., Chen, L., & Hu, R. (2011, October). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 157-164). ACM.
[17] Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70-84.
[18] Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960.
[19] Qiu, L., Gao, S., Cheng, W., & Guo, J. (2016). Knowledge-Base d Systems Aspect-based latent factor model by integrating ratings and European Scientific Journal December 2016 edition vol. 12, No. 34 ISSN: 1857–7881 (Print) e - ISSN 1857-7431 88 reviews for recommender system. Knowledge-Based Systems, 110, 233–243. doi: 10.1016/j.knosys.2016.07.033. Retrieved: 16/02.2021.
[20] Osho O., Onuoha C., Ugwu J., and Falaye A. (2016). E-Commerce in Nigeria: A Survey of Security Awareness of Buyers and Factors that Influence Acceptance. https://www.researchgate.net/publication/311589556_E-Commerce_in_Nigeria_A_Survey_of_Security_Awareness_of_Buyers_and_Factors_that_Influence_Acceptance Retrieved: 14/03.2021.
[21] Karimova F. (2016). A Survey of e-Commerce Recommender Systems. https://eujournal.org/index.php/esj/article/viewFile/8479/8082 Retrieved: 09/03.2021.
[22] Chen, S., Owusu, S., & Zhou, L. (2013). Social Network Based Recommendation Systems: A Short Survey. doi: 10.1109/SocialCom.2013.134 Retrieved: 9/02.2021.
Cite This Article
  • APA Style

    Olutosin Bukola Alabi, Alabi Olubunmi Funmilola. (2021). Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase. Control Science and Engineering, 5(2), 20-24. https://doi.org/10.11648/j.cse.20210502.11

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    ACS Style

    Olutosin Bukola Alabi; Alabi Olubunmi Funmilola. Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase. Control Sci. Eng. 2021, 5(2), 20-24. doi: 10.11648/j.cse.20210502.11

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    AMA Style

    Olutosin Bukola Alabi, Alabi Olubunmi Funmilola. Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase. Control Sci Eng. 2021;5(2):20-24. doi: 10.11648/j.cse.20210502.11

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  • @article{10.11648/j.cse.20210502.11,
      author = {Olutosin Bukola Alabi and Alabi Olubunmi Funmilola},
      title = {Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase},
      journal = {Control Science and Engineering},
      volume = {5},
      number = {2},
      pages = {20-24},
      doi = {10.11648/j.cse.20210502.11},
      url = {https://doi.org/10.11648/j.cse.20210502.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20210502.11},
      abstract = {Due to the huge amount of data available to buyers, the use of sophisticated algorithms can increase the revenue of ecommerce stores with modern recommender systems. The study was designed to investigate the impact of recommender systems on e-buyers online purchasing behaviors, and predict purchase patterns of buyers. The result of the study revealed how recommender systems affect shopping experience, increase sales for business owners and reach efficient product stocking and delivery. This research proposes an approach of increase in sales and the possibility of purchase prediction based on recommender systems. A survey of e-buyers was taken to determine the impact of recommender systems on past and future purchases. Results show that recommender systems improve shopping experience, increase purchase and can be a good tool to remind buyers of what they need to buy. It shows that recommender systems have the ability to predict what a buyer may be interested in purchasing. Based on the obtained user behavior and e-buyers satisfaction with recommender systems, e commerce stores can take advantage of this to send personalized recommended items to buyers’ emails to increase their sales. As e commerce shopping becomes more accepted globally, findings in this study have benefits to both shopping experience and sales enhancement.},
     year = {2021}
    }
    

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    T1  - Impact of Recommender Systems on E-customers Buying Patterns in Nigeria a Tool for Predicting Future Purchase
    AU  - Olutosin Bukola Alabi
    AU  - Alabi Olubunmi Funmilola
    Y1  - 2021/08/31
    PY  - 2021
    N1  - https://doi.org/10.11648/j.cse.20210502.11
    DO  - 10.11648/j.cse.20210502.11
    T2  - Control Science and Engineering
    JF  - Control Science and Engineering
    JO  - Control Science and Engineering
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.cse.20210502.11
    AB  - Due to the huge amount of data available to buyers, the use of sophisticated algorithms can increase the revenue of ecommerce stores with modern recommender systems. The study was designed to investigate the impact of recommender systems on e-buyers online purchasing behaviors, and predict purchase patterns of buyers. The result of the study revealed how recommender systems affect shopping experience, increase sales for business owners and reach efficient product stocking and delivery. This research proposes an approach of increase in sales and the possibility of purchase prediction based on recommender systems. A survey of e-buyers was taken to determine the impact of recommender systems on past and future purchases. Results show that recommender systems improve shopping experience, increase purchase and can be a good tool to remind buyers of what they need to buy. It shows that recommender systems have the ability to predict what a buyer may be interested in purchasing. Based on the obtained user behavior and e-buyers satisfaction with recommender systems, e commerce stores can take advantage of this to send personalized recommended items to buyers’ emails to increase their sales. As e commerce shopping becomes more accepted globally, findings in this study have benefits to both shopping experience and sales enhancement.
    VL  - 5
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Author Information
  • Computing and Mathematical Sciences Department, University of Greenwich, London, United Kingdom

  • Computer Science Department, African University of Science and Technology, Abuja, Nigeria

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