Abstract
When looking for a job, the job seeker registers in various job portals to get notifications or updates on different types of job circulars. Candidates from all types of job circulars can look for jobs according to their qualifications. To make this system faster and time-efficient, we set up a proposed system where a job seeker finds the similarity score of a Job Post with their resume after uploading it. Based on this resume, the candidate will be able to apply for the job post very quickly as the similarity score will be higher in the job post. This system matches the candidate’s resume skills, projects, and previous job responsibilities, with the properties of job posts like job description, job responsibilities, requirements, and measures similarity score. To measure the similarity score of Job Circular with this resume, we implement different models of NLP. The GloVe model gives the best accuracy which is 79.2% to all other models. We calculate this similarity through cosine similarity and euclidean distance. In this way, the employer will also be able to check these similarity scores and filter the candidates very quickly. Therefore we can say that our proposed system acts as a bridge between the employer and the job seeker.
A. Sultana—Supported by East Delta University.
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References
Huynh, T.V., Nguyen, K.V., Nguyen, N.L., Nguyen, G.: Job prediction: from deep neural network models to applications. In: RIVF International Conference on Computing and Communication Technologies (RIVF), pp 1–6 (2020)
Choudhary, S., Mishra, S.: Collaborative job prediction based on Naive Bayes classifier using python platform. In: International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp. 1–6 (2016)
Belsare, R.G., Deshmukh, V.M.: Employment recommendation system using matching, collaborative filtering, and content-based recommendation. Int. J. Comput. Appl. Technol. Res. 7(6), 215–220 (2018)
Khamker, N., Khamker, Y., Butwall, M.: Resume match system. Int. J. Innov. Sci. Res. Technol. Issue 5, 1–6 (2021)
Shamrat, F.J.M., Tasnim, Z., Ghosh, P., Majumder, A., Hasan, M.Z.,: Personalization of job circular announcement to applicants using decision tree classification algorithm. In: IEEE International Conference for Innovation in Technology (INOCON) Bengaluru, India, pp 1–6 (2020)
Calculating Document Similarities using BERT, word2vec, and other models. https://towardsdatascience.com/calculating-document-similarities-using-bert-and-other-models-b2c1a29c9630. Accessed 20 May 2022
Job Recommendation Engine. https://github.com/ggeop/Job-Recommendation-Engine. Accessed 20 May 2022
Find Text Similarities with your own Machine Learning Algorithm. https://towardsdatascience.com/find-text-similarities-with-your-own-machine-learning-algorithm-7ceda78f9710. Accessed 20 May 2022
Deep Learning for Semantic Text Matching. https://towardsdatascience.com/deep-learning-for-semantic-text-matching-d4df6c2cf4c5. Accessed 20 May 2022
Intuitive Guide to Understanding GloVe Embeddings. https://towardsdatascience.com/light-on-math-ml-intuitive-guide-to-understanding-GloVe-embeddings-b13b4f19c010. Accessed 20 May 2022
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Chowdhury, S.M.S., Chowdhury, M., Sultana, A. (2023). Matching Job Circular with Resume Using Different Natural Language Processing Based Algorithms. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_34
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