Analyzing Resume with AI – Get Hired
How to cite (IJASEIT) :
As competition for jobs intensifies, job seekers must focus on crafting descriptions that align well with organizations, making it imperative to have a proper resume. The investigation involves the development of an ML-driven Advanced Application Tracking System (ATS) that reviews resumes and provides in-depth feedback on their quality. It will analyze resumes for factors such as relevance to the job descriptions, keyword optimization, structure, and overall presentation. Some aspects include Resume evaluation, rating resumes as poor, good, or excellent based on preset parameters, and providing specific suggestions for improvement. It will then optimize keywords by identifying essential terms from a job description and validating their inclusion and contextual relevance in a candidate’s resume, ensuring alignment with automated HR screening protocols. By integrating a personality-prediction module, the system would analyze resumes using Natural Language Processing (NLP) and sentiment analysis to predict personality traits that may help employers assess whether the applicant will fit their particular company culture and team dynamics. With appropriate guidance, candidates can tailor their job applications to secure the most suitable employment opportunities. Moreover, this tool, built as a result of machine learning combined with natural processing, simplifies the hiring procedure, leading eventually to better resume quality for hiring candidates and consequently better candidate suitability on the recruiting party's side - all these result in the holistic efficiency of the hiring process.
C. Pina, J. Petersheim, J. N. Lahey, G. A. Alexander, and T. Hammond, "Using machine learning with eye-tracking data to predict if a recruiter will approve a resume," Mach. Learn. Knowl. Extr., vol. 5, no. 3, pp. 713–724, Jun. 2023, doi: 10.3390/make5030038.
A. McCallum and K. Nigam, "A comparison of event models for naive Bayes text classification," in Proc. AAAI-98 Workshop Learn. Text Categorization, Madison, WI, USA, 1998, pp. 41–48.
G. Zhou and J. Su, "Named entity recognition using an HMM-based chunk tagger," in Proc. 40th Annu. Meeting Assoc. Comput. Linguistics (ACL), Philadelphia, PA, USA, Jul. 2002, pp. 1–8, doi: 10.3115/1073083.1073163.
R. Grishman and A. E. Borthwick, "A maximum entropy approach to named entity recognition," New York Univ., New York, NY, USA, Comput. Sci. Dept. Tech. Rep. #NYU-CS-TR-1999-810, 1999.
C. A. Reddy, B. S. Jayanth, B. Rithvik, B. Avinash, and R. Tiwari, "Intelligent resume tracking system," J. Technol., vol. 12, no. 4, pp. 45–52, 2024.
A. Chavan, N. Tatewar, P. Naicker, and S. Deore, "AI resume analyzer," Int. J. Creative Res. Thoughts, vol. 11, no. 12, pp. h820–h829, Dec. 2023.
A. Sharma, N. Singh, and A. Kumar, "Automated resume matching and ranking using machine learning techniques," Int. J. Comput. Appl., vol. 188, no. 11, pp. 12–17, May 2021.
V. Prathima, A. Singh, T. Rohilla, V. Rebbavarapu, and N. Anjum, "Resume application tracking system with Google Gemini Pro," in Proc. Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), Coimbatore, India, 2024, pp. 1–6.
R. T. Fareed, R. V, and S. D. Kaganurmath, "Resume classification and ranking using KNN and cosine similarity," Int. J. Eng. Res. Technol., vol. 10, no. 08, pp. 401–405, Aug. 2021.
K. F. F. Jiechieu and N. Tsopze, "Skills prediction based on multi-label resume classification using CNN with model predictions explanation," Neural Comput. Appl., vol. 33, pp. 1–15, Aug. 2020, doi: 10.1007/s00521-020-05342-3.
J. Du and D. Ma, "Intelligent resume retrieval based on Lucene," J. Softw., vol. 17, no. 1, pp. 29–35, Jan. 2022, doi: 10.17706/jsw.17.1.29-35.
R. V. K. Bevara et al., "Transforming applicant tracking systems with intelligent resume embeddings for precise candidate matching," Dept. Inf. Sci., Univ. North Texas, Denton, TX, USA, Tech. Rep. UNT-IS-2024-01, 2024.
C. Dubey, M. Gajbhiye, N. Bomanwar, and A. Donald, "AI-based virtual assistant using Python: A system review," Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 5, pp. 5208–5213, May 2023.
S. G. M. and S. K. Suganthi, "AI based suitability measurement and prediction between job description and job seeker profiles," Atria Inst. Technol., Bengaluru, India, Tech. Rep., 2023.
K. Shivhare, S. Shakya, and A. S. Bhadouria, "A machine learning-powered web platform for resume building," Int. J. Res. Appl. Sci. Eng. Technol., vol. 12, no. 2, pp. 1801–1807, Feb. 2024, doi: 10.22214/ijraset.2024.61731.
A. Fapal, T. Kanade, B. Janrao, M. Kamble, and M. Raule, "Personal virtual assistant for Windows using Python," Int. Res. J. Mod. Eng. Technol. Sci., vol. 3, no. 7, pp. 2184–2190, Jul. 2021.
G. S. Dattatry, A. S. Prabhakar, M. S. Sandeep, R. T. Suryakant, and A. L. Kedar, "Job recommendation by skill matching using ML," Int. J. Res. Publ. Rev., vol. 4, no. 4, pp. 2392–2397, Apr. 2023.
S. Jadhav, S. Kawade, and S. Chikhale, "ICT: Resume builder system using full stack web development," Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 6, pp. 1678–1683, Jun. 2023.
N. Sivaramakrishnan, V. Subramaniyaswamy, S. Arunkumar, and P. S. Rathna, "Validating effective resume based on employer's interest with recommendation system," Int. J. Pure Appl. Math., vol. 119, no. 12e, pp. 13261–13272, 2018.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Attribution-ShareAlike 4.0 International License
https://creativecommons.org/licenses/by-sa/4.0/