NeoHire: A Neo4j-Based Intelligent Recruitment System
DOI:
https://doi.org/10.63503/acset.140Keywords:
Recruitment System, Neo4j, Resume Parsing, Machine Learning, Natural Language Processing, Semantic Similarity, Candidate Ranking, Graph DatabaseAbstract
Even though many organisations have worked on their recruitment systems over the years, a considerable part of the hiring process still relies on manually scanning resumes and using keywords to filter candidates they want to hire. All of these methods can help limit basic screening effort, but don’t always identify candidates with the skills and experience to match a job role based on an exact keyword match. With hundreds of applications for a single position, recruiters are expected to shortlist within a limited time, which can be a repetitive, time-consuming, and sometimes inconsistent process. This paper presents a novel recruitment system, called NeoHire, which utilises NLP, ML, and graph database technologies to enhance candidate screening. The system emphasizes the semantics behind resumes and job descriptions over keyword matching by applying the technique of semantic relationships to candidate resumes and job descriptions. The data of the candidate’s resume, including skills, education and work experience, communications and other important details, is extracted by NeoHire from a variety of resume formats and subsequently stored in a Neo4j graph structure. Transformer-based embedding models are employed to assess the contextual similarity between resumes and job requirements, thereby enhancing matching accuracy. The graph-based system also facilitates a better understanding of the connections among candidates, skills, and job roles. The intended system is designed to minimize the manual screening time, enhance screening transparency in the shortlisting process, and offer a scalable recruitment system that fits more closely with the needs of modern recruitment processes.
References
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson Education, 2020.
[2] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Pearson Education, 2021.
[3] C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval. Cambridge University Press, 2008.
[4] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” in Proc. ICLR, 2013.
[5] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL, 2019.
[6] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” in Proc. EMNLP, 2019.
[7] Neo4j Inc., Graph Database Platform: Concepts and Applications, 2022.
[8] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009.
[9] C. C. Aggarwal, Machine Learning for Text. Springer, 2018.
[10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
[11] J. Leskovec, A. Rajaraman, and J. Ullman, Mining of Massive Datasets. Cambridge University Press, 2020.
[12] E. Alpaydin, Introduction to Machine Learning, 4th ed. MIT Press, 2020.
[13] M. A. Hearst, Search User Interfaces. Cambridge University Press, 2009.
[14] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2009.
[15] J. D. Kelleher and B. Mac Namee, Fundamentals of Machine Learning for Predictive Data Analytics. MIT Press, 2015.
[16] A. Vaswani et al., “Attention Is All You Need,” in Proc. NeurIPS, 2017.
[17] T. Brown et al., “Language Models are Few-Shot Learners,” in Proc. NeurIPS, 2020.
[18] J. Pennington, R. Socher, and C. Manning, “GloVe: Global Vectors for Word Representation,” in Proc. EMNLP, 2014.
[19] F. Chollet, Deep Learning with Python, 2nd ed. Manning Publications, 2021.
[20] Y. Goldberg, Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers, 2017.
[21] A. Rajaraman and J. Ullman, Mining of Massive Datasets. Cambridge University Press, 2011.
[22] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. MIT Press, 2018.
[23] I. Witten, E. Frank, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2016.
[24] C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
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