International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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India | Computer Science Engineering | Volume 13 Issue 8, August 2024 | Pages: 604 - 607


Recommender System for Telecom Product and Services

Gaurav Gupta, Abdul Baes

Abstract: This paper presents the development and implementation of a personalized recommender system for Etisalat's product and services department, specifically targeting internet data bundles. By leveraging extensive customer data, including demographic information, subscription details, internet usage patterns, and customer behavior, the system aims to provide highly accurate recommendations that align closely with individual customer interests. The recommender system is built using TensorFlow, an open - source machine learning framework, to ensure robust performance and scalability. The main goal is to keep the existing customer satisfied and acquire new customers and gain market competence in hand. Our results demonstrate significant improvements in recommendation precision and customer satisfaction, highlighting the potential of machine learning in enhancing customer experience in the telecom industry.

Keywords: Recommender System, Etisalat, Telecom, Feature Engineering, TensorFlow, Customer Satisfaction

How to Cite?: Gaurav Gupta, Abdul Baes, "Recommender System for Telecom Product and Services", Volume 13 Issue 8, August 2024, International Journal of Science and Research (IJSR), Pages: 604-607, https://www.ijsr.net/getabstract.php?paperid=SR24807201535, DOI: https://dx.doi.org/10.21275/SR24807201535


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Received Comments

Mr. Mustafa Zahaak Rating: 9/10 😊
2024-08-12
The Most usefull paper for recommendation system implementation in telecom domain.
Mr. Mohammad Azim Rating: 10/10 😊
2024-08-12
Great and real life project. Great effort and research
Mr. Abdullah Hashemi Rating: 10/10 😊
2024-09-03
Very use full and practical in real world project which is used fully for telecom domain
Mr. Shokur Zahin Rating: 10/10 😊
2024-09-03
This paper on the recommendation system is excellent It provides a comprehensive analysis of various part and presents innovative approaches to improving accuracy and user satisfaction.
Ms. Rawina Rating: 10/10 😊
2024-09-04
Clearly explained the project with desirable usage in telecom.

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