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

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A Hybrid Recommendation System For University Selection Using Machine Learning

Authors
Harsh Motiramani1, *, Vedaant Melkari1, Malav Mehta1
1Student, SVKM’s NMIMS Mukesh Patel School of Technology Management and Engineering, Mumbai, MH, India
*Corresponding author. Email: motiramani23@gmail.com
Corresponding Author
Harsh Motiramani
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_265How to use a DOI?
Keywords
Match Score; XGBoost; Regression; Cosine Similarity; Hybrid Model
Abstract

It is important to consider multiple factors, including academic fit, finances, and personal preferences, while deciding to select a university. For this, we propose a hybrid recommendation system that uses Machine Learning (ML) and Natural Language Processing (NLP). Using acceptance rates, tuition fees, GPA, and GRE scores, our system predicts a match score using XGBoost. Cosine similarity is applied to evaluate unstructured data, including course descriptions, to align students’ interests with universities. Institutional reputation is assessed through sentiment analysis of reviews, for which a comparison tool allows side-by-side evaluations. An application assistance module monitors all deadlines, and tailored Statements of Purpose (SOPS) are generated through template-based NLP frameworks. The proposed system offers a personalised approach by providing recommendations through the integration of quantitative and qualitative data. The operational efficacy of the system has been tested and proves to aid students in making informed decisions. This work extends the development of AI educational tools by creating a scalable system for incorporating university recommendation systems.

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_265How 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  - Harsh Motiramani
AU  - Vedaant Melkari
AU  - Malav Mehta
PY  - 2025
DA  - 2025/11/04
TI  - A Hybrid Recommendation System For University Selection Using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3174
EP  - 3193
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_265
DO  - 10.2991/978-94-6463-858-5_265
ID  - Motiramani2025
ER  -