SmartResume AI: An Intelligent Resume Analysis System Using Machine Learning and Natural Language Processing

This paper presented SmartResume AI, an integrated, explainable resume-analysis platform that combines transformer-based NLP with machine learning to deliver ATS score prediction, resume quality assessment, semantic skill-gap analysis, resume comparison, job recommendation, automated interview-question generation, and a conversational AI career assistant. A working prototype implementing this pipeline has been developed, and its architecture, algorithms, and system design have been described in detail, together with the evaluation methodology that will be used to validate it against a keyword-matching baseline. The central contribution of this work is architectural: by decomposing ATS scoring into explainable, feature-level attributions, matching skills semantically rather than lexically, and connecting the resulting analysis to job recommendation, resume comparison, interview preparation, and conversational guidance within one pipeline, SmartResume AI addresses gaps that, individually, are only partially addressed by the systems reviewed in Section II. Confirming the practical benefit of this design over existing tools through the controlled evaluation described in Section IX is the immediate next step for this work.

[1] Y. Liang, Z. Lin, X. Shi, and H. Ma, "Efficient multi-task deep learning for resume parsing," in Proc. IEEE Int. Conf. on Big Data, 2023. [2] S. Gopinath, U. S., and K. C. R., "Resume parsing and job prediction," in Proc. IEEE Int. Conf. on Computer Vision Workshops, 2024. [Citation details reported by secondary source; verify against publisher record before submission.] [3] X. Zhang, T. Wang, Y. Yang, X. Shen, Y. Bu, Y. He, and Y. Ding, "Resume recommendation method using decision trees," in Proc. IEEE Int. Conf. on Data Mining, 2024. [Citation details reported by secondary source; verify against publisher record before submission.] [4] S. P. Warusawithana, N. N. Perera, R. L. Weerasinghe, T. M. Hindakaraldeniya, and G. U. Ganegoda, "Layout-aware resume parsing system based on NLP," in Proc. IEEE Int. Conf. on Machine Learning and Applications, 2023. [5] R. Ransing, S. Mohan, M. Emberi, and M. Mahavarkar, "Stacked resume screening using deep learning and machine learning ensembles," Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, 2021. [6] Spoorthi M., et al., "An ensemble deep learning approach for resume classification," Int. J. Eng. Res. Technol., vol. 12, no. 3, 2023. [7] A. Bhatt, "AI-driven resume analyser using MapReduce and KNN classification," Int. J. Comput. Appl., vol. 186, no. 14, 2024. [8] S. Bharadwaj, "Resume screening using NLP and LSTM networks," Int. J. Comput. Sci. Mobile Comput., vol. 11, no. 5, 2022. [9] U. K. Jaiswal, A. Gupta, M. Dwivedi, M. S. Shamsi, and A. Agarwal, "Predicting professional personality from resumes using BERT and XLNet," Adv. Data Sci. Eng., vol. 23, no. 2, pp. 212–225, 2024. [10] A. Garcia and L. Fernandez, "Intelligent ATS scoring and skill-gap analysis using NLP and deep learning," IEEE Access, vol. 12, 2024. [Page range reported by secondary source could not be independently confirmed; verify before submission.] [11] Y. Zhao, H. Xu, and X. Liu, "Transformer-based semantic matching for recruitment applications," Knowledge-Based Syst., vol. 284, 2024. [12] P. Bhandari, C. R. Patil, C. S. Patil, S. Deshmukh, and R. Badre, "AI-driven resume screening: Automating the recruitment process," 2024. [Journal/venue name reported by secondary source could not be independently confirmed; verify before submission.] [13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, "Attention is all you need," in Proc. NeurIPS, 2017. [14] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proc. NAACL-HLT, 2019. [15] N. Reimers and I. Gurevych, "Sentence-BERT: Sentence embeddings using Siamese BERT-networks," in Proc. EMNLP-IJCNLP, 2019. [16] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016. [17] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Proc. NeurIPS, 2017. [18] P. Lewis et al., "Retrieval-augmented generation for knowledge-intensive NLP tasks," in Proc. NeurIPS, 2020.