Role Radar: An AI-Based Resume Intelligence and Skill Gap Analysis System

[1] C. Qin, H. Zhu, T. Xu, C. Zhu, C. Ma, E. Chen, and H. Xiong, “An enhanced neural network approach to person-job fit in talent recruitment,” ACM Trans. Inf. Syst., vol. 38, no. 2, pp. 15:1–15:33, 2020.
[2] Z. Ji et al., “Survey of hallucination in natural language generation,” ACM Comput. Surv., vol. 55, no. 12, pp. 248:1–248:38, 2023.
[3] M. Raghavan, S. Barocas, J. Kleinberg, and K. Levy, “Mitigating bias in algorithmic hiring: Evaluating claims and practices,” in Proc. ACM Conf. Fairness, Accountability, and Transparency (FAT*), 2020.
[4] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474.
[5] J. Ha, R. M. Haralick, and I. T. Phillips, “Recursive X-Y cut using bounding boxes of connected components,” in Proc. 3rd Int. Conf. Document Analysis and Recognition (ICDAR), Montreal, Canada, Aug. 1995, pp. 952–955.
[6] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. Conf. Empirical Methods in Natural Language Processing and 9th Int. Joint Conf. Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, Nov. 2019, pp. 3982–3992.
[7] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manage., vol. 24, no. 5, pp. 513–523, 1988.
[8] A. B. Kahn, “Topological sorting of large networks,” Commun. ACM, vol. 5, no. 11, pp. 558–562, Nov. 1962.
[9] Greenhouse Software, “Job Board API.” [Online]. Available: https://developers.greenhouse.io/job-board.html
[10] A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
[11] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 4171–4186, doi: 10.18653/v1/N19-1423.
[12] M. Zhang, K. N. Jensen, S. D. Sonniks, and B. Plank, “SkillSpan: Hard and soft skill extraction from English job postings,” in Proc. 2022 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, WA, USA, 2022,pp. 4962–4984, doi: 10.18653/v1/2022.naacl-main.366.
[13] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778.
[14] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
[15] T. Green, D. Maynard, and C. Lin, “Development of a benchmark corpus to support entity recognition in job descriptions,” in Proc. 13th Language Resources and Evaluation Conference, Marseille, France, 2022.
[16] A.-S. Gnehm, E. Bühlmann, H. Buchs, and S. Clematide, “Fine-grained extraction and classification of skill requirements in German-speaking job ads,” in Proc. Fifth Workshop on Natural Language Processing and Computational Social Science, Abu Dhabi, UAE, 2022,pp. 14–24
[17] E. Tabassi, “Artificial intelligence risk management framework (AI RMF 1.0),” National Institute of Standards and Technology, Gaithersburg, MD, USA, NIST AI 100-1, 2023, doi: 10.6028/NIST.AI.100-1.