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

Olutosin Bukola Alabi; Alabi Olubunmi Funmilola

doi:doi:10.11648/j.cse.20210502.11

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

Olutosin Bukola Alabi, Alabi Olubunmi Funmilola

Published in Control Science and Engineering (Volume 5, Issue 2)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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.
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[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.
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[14]	Ricci, F.; Rokach, L.; Shapira, B. Introduction to recommender systems handbook. In Recommender Systems Handbook; Springer: Boston, MA, USA, 2011; pp. 1–35.
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[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.
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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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TY  - JOUR
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
SP  - 20
EP  - 24
PB  - Science Publishing Group
SN  - 2994-7421
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
IS  - 2
ER  -

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Author Information

Olutosin Bukola Alabi

Computing and Mathematical Sciences Department, University of Greenwich, London, United Kingdom
Alabi Olubunmi Funmilola

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

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Plain Text BibTeX RIS

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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TY  - JOUR
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
SP  - 20
EP  - 24
PB  - Science Publishing Group
SN  - 2994-7421
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
IS  - 2
ER  -

Copy | Download