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

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Smart Phone Recommendation System Using Machine Learning

Authors
K. Satyanarayana Murthy1, K. Siri Varshini1, *, B. Kamal Sai Kushvanth1, Md Nihaal1, K. RamaSai1
1Department of Information Technology, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
*Corresponding author. Email: sirivarshinikarri@gmail.com
Corresponding Author
K. Siri Varshini
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_78How to use a DOI?
Keywords
Smartphone Recommendation System(SRS); Machine Learning; Content Based Filtering (CBF); Collaborative Filtering (CF); Fast API; React.js; Npm
Abstract

The purpose of the Smartphone Recommendation System (SRS) is to create personalized smartphone suggestionsusing Machine Learning (ML) approaches. The system utilizes a hybrid recommendation strategy based on Content-Based Filtering (CBF), recommending devices by identifying similarities based on documented features, as well as Collaborative Filtering (CF) by analyzing user preferences and suggesting based on users selecting similar devices. This product is developed using FastAPI for the backend and React.js for the frontend, which provides an interactive and scalable web experience. In addition, the recommendation engine pulls from a serialized ML model and a dataset of features to provide users with real-time personalized suggestions. Users can further narrow preferences, receive recommendations that can adapt over time, and use a recommendation engine that continues to collect data to improve suggestions. The front end also uses npm to deploy, providing good user experience while using the system. Users can edit their preferences, enjoy adaptive suggestions, and make the most out of an automatically enhancing system. The frontend application is hosted through npm, with the promise of a smooth and user-friendly interface.

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_78How 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  - K. Satyanarayana Murthy
AU  - K. Siri Varshini
AU  - B. Kamal Sai Kushvanth
AU  - Md Nihaal
AU  - K. RamaSai
PY  - 2025
DA  - 2025/11/04
TI  - Smart Phone Recommendation System Using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 929
EP  - 936
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_78
DO  - 10.2991/978-94-6463-858-5_78
ID  - Murthy2025
ER  -