Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Machine Learning on User Profiles and Market Trends for Job Recommendations

Authors
M. Narasimha Raju1, *, Polisettti Mohana Lakshmi Rupa1, Poturi M. Lakshmi Sree Harshitha1, Shaik Jasmine1, Vislavath Pavani1, Yeddu Leena Rishitha1
1Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India, 534202
*Corresponding author. Email: mnraju234@gmail.com
Corresponding Author
M. Narasimha Raju
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_41How to use a DOI?
Keywords
job recommendation; machine learning; user profiles; market trends; web platform; clustering; similarity matching; dashboard interface; career insights; scalability
Abstract

Technological advancements and shifting industry demands have caused job markets to change quickly, making it difficult for job searchers to find positions that align with their goals and skill set. Conventional recommendation systems frequently fall short because they rely on out-of-date data and ignore personal preferences or current market trends. This study presents a web-based job recommendation system that uses machine learning to seamlessly combine comprehensive user profiles with the most recent market insights in order to address these issues. Users register, log in, and use an easy-to-use dashboard to provide their desired locations, experience, skills, and expected salaries. This information is combined with a sizable collection of job advertisements that were scraped from Naukri and processed using a combination of similarity and clustering techniques to provide tailored job recommendations. Powered by a Flask backend, PostgreSQL database, and HTML, CSS, and JavaScript frontend, the platform is enhanced with email notifications, job-saving choices, and insights into upskilling opportunities and trending technologies. It far outperforms traditional approaches and gives job searchers a useful tool to succeed in changing work environments.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_41How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Narasimha Raju
AU  - Polisettti Mohana Lakshmi Rupa
AU  - Poturi M. Lakshmi Sree Harshitha
AU  - Shaik Jasmine
AU  - Vislavath Pavani
AU  - Yeddu Leena Rishitha
PY  - 2025
DA  - 2025/11/04
TI  - Machine Learning on User Profiles and Market Trends for Job Recommendations
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 470
EP  - 481
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_41
DO  - 10.2991/978-94-6463-858-5_41
ID  - Raju2025
ER  -