OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
E-Commerce Customer Churn Prediction Scheme Based on Customer Behaviour Using Machine Learning
P. Nagaraj, V. Muneeswaran, A Dharanidharan, et al.
2022 International Conference on Computer Communication and Informatics (ICCCI) (2023)
Closed Access | Times Cited: 10
P. Nagaraj, V. Muneeswaran, A Dharanidharan, et al.
2022 International Conference on Computer Communication and Informatics (ICCCI) (2023)
Closed Access | Times Cited: 10
Showing 10 citing articles:
DIGITAL BUSINESS TRANSFORMATION: ANALYSIS OF THE EFFECT ARTIFICIAL INTELLIGENCE IN E-COMMERCE’S PRODUCT RECOMMENDATION
Jeffry Vincent Louis, Noerlina Noerlina, Dicky Hida Syahchari
Advanced Information Systems (2024) Vol. 8, Iss. 1, pp. 64-69
Open Access | Times Cited: 1
Jeffry Vincent Louis, Noerlina Noerlina, Dicky Hida Syahchari
Advanced Information Systems (2024) Vol. 8, Iss. 1, pp. 64-69
Open Access | Times Cited: 1
Can a simple customer review outperform a feature set for predicting churn?
William Jones Beckhauser, Renato Fileto
(2024), pp. 117-128
Closed Access | Times Cited: 1
William Jones Beckhauser, Renato Fileto
(2024), pp. 117-128
Closed Access | Times Cited: 1
Application of Machine learning in Churn prediction contexts – A literature review
Pedro Fernandes
(2024)
Open Access
Pedro Fernandes
(2024)
Open Access
Identification of an Efficient Machine Learning Algorithm for the Prediction of Customer Churn: A Case Analysis of a Business-to-Consumer (B2C) Dairy Company for Customer Retention Strategy
A. Mohammed Faisal, V. Rajkiran, K. Sankar Singh, et al.
(2024), pp. 1-5
Closed Access
A. Mohammed Faisal, V. Rajkiran, K. Sankar Singh, et al.
(2024), pp. 1-5
Closed Access
Customer Segmentation Using Supervised and Unsupervised Machine Learning Techniques
P. Nagaraj, C Bharath Kumar, K. Charan Kumar, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 1
P. Nagaraj, C Bharath Kumar, K. Charan Kumar, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 1
Unveiling E-Commerce User Behavior through Deep Learning Evolutionary Data Mining
C. Jayanthi, A. Rukmani, D Annalakshmi, et al.
(2023), pp. 1-7
Closed Access
C. Jayanthi, A. Rukmani, D Annalakshmi, et al.
(2023), pp. 1-7
Closed Access
Stock Market Profit Prediction Using Machine Learning Algorithms and Visualization for Live Data
P. Nagaraj, K Nani, E Teja Krishna, et al.
(2023), pp. 1-6
Closed Access
P. Nagaraj, K Nani, E Teja Krishna, et al.
(2023), pp. 1-6
Closed Access
Loan Prediction Analysis Using Innumerable Machine Learning Algorithms
P. Nagaraj, K Nikhil, Kishore Babu, et al.
(2023), pp. 1-6
Closed Access
P. Nagaraj, K Nikhil, Kishore Babu, et al.
(2023), pp. 1-6
Closed Access
Customer Sale Analysis and Classification Using Machine Learning Algorithm
P. Nagaraj, K Nani, E Teja Krishna, et al.
(2023), pp. 1-5
Closed Access
P. Nagaraj, K Nani, E Teja Krishna, et al.
(2023), pp. 1-5
Closed Access
CusCP: AI-Driven System for Predictive Modeling of Customer Churn in E-commerce Using Machine Learning
Jagdeep Sharma, Shantanu Neema
(2023), pp. 924-929
Closed Access
Jagdeep Sharma, Shantanu Neema
(2023), pp. 924-929
Closed Access
