Enhancing E-commerce with Collaborative Filtering: Challenges and an Overview

AUTHOR(S)

Anilamol MA, Afia Ashraf, Jinta Mariya Thomas, Linta Maria Thomas, Dr. Susheel George Joseph

DOI: https://doi.org/10.46647/ijetms.2023.v07i04.066

ABSTRACT
Digital marketing is experiencing a rapid growth in the present era, as we all are heading towards a digital world. People have started to become completely involved in digital things, which have slowly made them interact with digital marketing. So here arises a question: is it possible to do marketing in this digital world by knowing the user interest? Yes, if we get to know more about the user behavior towards digital marketing we can easily understand user interest and this is achieved with the help of “Recommendation Systems in machine learning”. All of us are familiar with the term recommendation system. It is a system that filters information in order to predict the rating or interest we have in an item. In this paper, we are going to analyze how the machine learning algorithm helps in the implementation of recommendation systems and here we are choosing collaborative filtering (CF) as a type of recommendation system to study the working of ML algorithms. Also, here we will have an overview about the role of CF in E-commerce and the advantages provided by CF. Lastly in this paper we will be discussing the challenges faced by Collaborative Filtering (CF) and how we can solve these challenges.
Page No: 503 - 506

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How to Cite This Article:
Anilamol MA, Afia Ashraf, Jinta Mariya Thomas, Linta Maria Thomas, Dr. Susheel George Joseph . Enhancing E-commerce with Collaborative Filtering: Challenges and an Overview . ijetms;7(4):503-506. DOI: 10.46647/ijetms.2023.v07i04.066