Enhancing E-Commerce Applications with Machine Learning Recommendation Systems

Authors

  • Rafey Ahmed Farooqi  Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
  • Surabhi Kesarwani  Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
  • Mohd Shakeeb  Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
  • Nitin Sharma  Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
  • Ishita Bhatnagar  Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India

DOI:

https://doi.org//10.32628/IJSRSET122935

Keywords:

E-Commerce Websites, Database Management, Classifiers, Machine Learning, Recommendation Systems, Content-Based, Collaborative filtering, Hybrid.

Abstract

In today’s times everything has moved to a digital platform. Even commerce has moved to a digital mode with people now preferring to buy things online rather than going to a physical store. Recommendation Systems are used in such platforms to help users. Recommendation System is one of the most popular application of Machine Learning with various techniques and algorithms to implement it. We have researched these algorithms and have presented an analysis by taking various factors into consideration.

References

  1. Hernandez, Sergio, et al. "Analysis of users’ behavior in structured e-commerce websites." IEEE Access 5 (2017): 11941-11958.
  2. Parallel and Sequential Recommendation System Architecture. Image provided by C.C. Aggarwal, Recommender Systems: The Textbook
  3. C.C Aggarwal “Recommender Systems: The Textbook” ISBN: 978-3-319-29659-3
  4. Sharma, Lalita, and Anju Gera. "A survey of recommendation system: Research challenges." International Journal of Engineering Trends and Technology (IJETT) 4.5 (2013): 1989- 1992.
  5. Isinkaye, Folasade Olubusola, Yetunde O. Folajimi, and Bolande Adefowoke Ojokoh. "Recommendation systems: Principles, methods and evaluation." Egyptian informatics journal 16.3 (2015): 261-273.
  6. Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009).
  7. Van Meteren, Robin, and Maarten Van Someren. "Using content-based filtering for recommendation." Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop. Vol. 30. 2000.
  8. www.towardsdatascience.com

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Rafey Ahmed Farooqi, Surabhi Kesarwani, Mohd Shakeeb, Nitin Sharma, Ishita Bhatnagar, " Enhancing E-Commerce Applications with Machine Learning Recommendation Systems, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.85-90, May-June-2022. Available at doi : https://doi.org/10.32628/IJSRSET122935