Publication:
AI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment

dc.contributor.authorYAZICI, MEHMET BATUHAN
dc.contributor.authorSABAZ, DAMLA
dc.contributor.authorELMASRY, WİSAM
dc.date.accessioned2024-12-19T08:12:53Z
dc.date.available2024-12-19T08:12:53Z
dc.date.issued2024
dc.description▪️ Date of Conference: 21-22 September 2024.
dc.description.abstractThis paper presents a resume-ranking web application that improves recruitment through advanced deep-learning techniques. The system uses the YOLOv9 model fine-tuned with our newly created custom dataset for segment detection on resumes of various structures, EasyOCR for text recognition, mBERT fine-tuned for text classification, and GLiNER for named entity recognition with regular expressions. These models and techniques efficiently extract, categorize, and match resume information with job descriptions. We created a custom dataset for our object detection training, and while we trained three models, YOLOv9 achieved the highest performance with a score of 0.84 mAP. Our hybrid matching approach provides highly accurate and relevant resume rankings using the embedding model, gte-large-en-v1.5, and cosine similarity for semantic matching with dense vectors with extracted keywords and BM25 for keyword relevance. The web application allows HR professionals to upload resumes seamlessly, define job descriptions, and view ranked results, providing a tailored solution to specific recruitment needs. Although we faced challenges such as text extraction accuracy and zero-shot NER limitations, our system demonstrated a solid overall performance. This paper demonstrates the potential of state-of-the-art deep learning models to enhance recruitment processes and provides a valuable tool for HR professionals to identify the most suitable candidates efficiently. © 2024 IEEE.en
dc.identifier.citationM. B. Yazıcı, D. Sabaz and W. Elmasry, "AI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment," 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkiye, 2024, pp. 1-8.
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207877839
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710945
dc.identifier.urihttps://hdl.handle.net/11413/9347
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.journal8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectArtificial Intelligence
dc.subjectNamed Entity Recognition
dc.subjectNatural Language Processing
dc.subjectOptical Character Recognition
dc.subjectText Recognition
dc.titleAI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitmenten
dc.title.alternative8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024en
dc.typeconferenceObject
dspace.entity.typePublication
local.indexed.atscopus
local.journal.endpage8
local.journal.startpage1
relation.isAuthorOfPublication2df31c35-4520-474f-ab80-a73b8e6a3b11
relation.isAuthorOfPublication.latestForDiscovery2df31c35-4520-474f-ab80-a73b8e6a3b11

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