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Paper ID:IJSREM65163

HIREWISE: ML-DRIVEN JOB RECOMMENDATION AND PREPARATION PLATFORM

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65163

Volume:

10 |

6 |

Jun 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PK

Prof.Arunadevi Khaple

Department of Computer Engineering

Zeal College Of Engineering And Research

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TJ

Tushar Jadhav

Department of Computer Engineering

Zeal College Of Engineering And Research

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AD

Aniket Digraskar

Department of Computer Engineering

Zeal College Of Engineering And Research

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SB

Shubham Bangar

Department of Computer Engineering

Zeal College Of Engineering And Research

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SD

Sangram Devadhe

Department of Computer Engineering

Zeal College Of Engineering And Research

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Abstract

The job market today is more competitive and complex than ever before, making it increasingly difficult for job seekers particularly fresh graduates and early-career professionals to find suitable opportunities and prepare effectively for the hiring process. Hirewise is a highly innovative, machine learning ML -driven platform designed to address these challenges by offering a seamless, end-to-end solution for job search and preparation. The platform leverages advanced ML techniques and natural language processing NLP to analyze user profiles, including skills, experience, and preferences, to generate highly personalized job recommendations that align closely with each individual s strengths and career goals. In addition to job matching, Hirewise provides a comprehensive suite of preparation tools, such as mock interviews, communication and confidence assessments, and AI-driven skill evaluations, enabling users to practice and improve their performance in real-world hiring scenarios. What sets Hirewise apart is its holistic approach it not only suggests relevant job opportunities but also helps users become more prepared and confident candidates through continuous feedback and learning. By integrating data-driven insights and intelligent automation, the platform enhances job fit, boosts user readiness, and increases the chances of securing desired job positions.

KEYWORDS

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Cite This Article

Prof.Arunadevi Khaple, Tushar Jadhav, Aniket Digraskar, Shubham Bangar, Sangram Devadhe, "HIREWISE: ML-DRIVEN JOB RECOMMENDATION AND PREPARATION PLATFORM," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-4, Jun 2026.

Full Article/Conclusion

"HireWise" aims to revolutionize the job search and preparation process by offering a comprehensive, data driven platform tailored to the needs of today’s job seekers. By integrating advanced profile analysis, machine learning–based job matching, personalized preparation tools, and real-time skill assessments, HireWise delivers a highly customized and effective approach to career advancement. As the platform continues toevolve, future enhancements will focus on improving the accuracy of job recommendations, expanding the range of preparation resources, and refining skill assessment features to ensure a seamless and empowering user experience.

References

1.      S.R. Rimitha, V. Abburu, A. Kiranmai, K. Chandrasekaran, "Ontologies to Model User Profiles in Personalized Job Recommendation," IEEE, vol. 2018, pp. 1-10, ref. no. 1, Jan. 2018. 2.       Q.-D. Nguyen, "Adaptive Methods for Job Recommendation Based on User Clustering," IEEE, vol. 2016, pp. 1-8, ref. no. 2, Aug. 2016. 3.  N.D. Almalis, G.A. Tsihrintzis, N. Karagiannis, A.D. Strati, "A Content-Based Job Recommendation Algorithm for Job Seeking and Recruiting," IEEE, vol. 2016, pp. 15-22, ref. no. 3, May 2016. 4.  W. Shalaby, B. AlAila, M. Korayem, L. Pournajaf, K. AlJadda, S. Quinn, W. Zadrozny, "A Graphbased Approach for Job Recommendation at Scale," IEEE, vol. 2017, pp. 30-37, ref. no. 4, Nov. 2017. 5.    E. Kasano, S. Muramatsu, A. Matsufuji, E. Sato-Shimokawara, T. Yamaguchi, "Estimation of Speaker’s Confidence in Conversation Using Speech Information and Head Motion," IEEE, vol. 2019, pp. 5-14, ref. no. 5, Sep.2019. 6.      R. Mandal, P. Lohar, D. Patil, A. Patil, S. Wagh, "AIBased Mock Interview Evaluator: An Emotion and Confidence Classifier Model," IEEE, vol. 2023, pp. 18-26, ref. no. 6, Mar. 2023. 7.   Y.-C. Chou, F.R. Wongso, C.-Y. Chao, H.-Y. Yu, "An AI Mock-interview Platform for Interview Performance Analysis," IEEE, vol. 2022, pp. 10-17, ref. no. 7, Dec. 2022

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Publication Date

6/24/2026

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Review Rounds: 2

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