Systematic Review of Methods for Analysis of Resumes

DOI

10.2991/978-94-6463-858-5_27How to use a DOI?

Keywords

Resume; Online application; accuracy; scalability; prediction

Abstract

Online applications for jobs have made recruitment complex, introducing problems such as dataset bias, unbalanced resume formats, and even heavy computational requirements. This paper surveys 44 peer-reviewed studies on methodologies, applications, and limitations of resume analyzers, explaining techniques like classification algorithms, neural networks, and hybrid systems. Recent tools can now achieve the best accuracy results of 85–94% in tasks that include parsing and ranking and even domain prediction, though the performance varies based on data quality and diversity. Emerging trends like multimodal analysis and real-time recommendation hold great promise but fall under the ambit of scalability and bias. The paper concludes with solutions and innovations that can improve the robustness and inclusivity of resume analyzers.

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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TY  - CONF
AU  - Darsh Kanikar
AU  - Pratyush Jain
AU  - Siddhi Jain
AU  - Lalit Purohit
PY  - 2025
DA  - 2025/11/04
TI  - Systematic Review of Methods for Analysis of Resumes
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 302
EP  - 316
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_27
DO  - 10.2991/978-94-6463-858-5_27
ID  - Kanikar2025
ER  -