학술논문

A Novel Approach to Recommend Products in E-Commerce
Document Type
Conference
Source
2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) ICISSGT Intelligent Systems, Smart and Green Technologies (ICISSGT), 2021 IEEE International Conference on. :17-21 Nov, 2021
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Conferences
Collaborative filtering
Electronic commerce
History
Intelligent systems
Machine Learning
K-Means Clustering
Collaborative Filtering
Recommendation System
Language
Abstract
Electronic commerce (e-commerce) refers to the purchasing and selling of products via the web. E-commerce platforms have been used by people around the globe in some form or another because everything can be purchased online with just a few mouse clicks. Since, there is a huge amount of data on every e-commerce site; a consumer may struggle to identify the product they require. In this scenario, the Recommendation System is used. A product's recommendation might be based on a variety of variables, including past search or purchase history, user reviews, and the most popular product. We have several Machine Learning-based approaches for these recommendations. We shall implement Collaborative Filtering and K-Means clustering in this work. We utilized Jupyter Notebook to develop the recommendation system, and the Amazon-ratings dataset from Kaggle was used. We will also examine numerous other recommendation techniques in this paper.