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ResumAI: Revolutionizing Automated Resume Analysis and Recommendation with Multi-Model Intelligence

Publication Type : Conference Paper

Publisher : IEEE

Source : 2023 Global Conference on Information Technologies and Communications (GCITC)

Url : https://ieeexplore.ieee.org/document/10426042

Campus : Amritapuri

School : School of Computing

Year : 2023

Abstract : Finding and organizing suitable candidates for a vacant job position can pose challenges, especially when there is a high volume of submissions. This can impede team growth as it becomes challenging to identify the most suitable individual in a timely manner. To address this issue, an automated system called "Resume Recommendation and Classification" can expedite the selection process and aid in decision-making, while also reducing the time-consuming task of fair screening and shortlisting. This research utilizes Natural Language Processing to collect resumes and extract the necessary information. Subsequently, the resumes are ranked using BERT and TF-IDF word embeddings to obtain similarity scores. A classification model is then developed to categorize resumes based on job designations. To categorize the resumes, various techniques were employed, including Multinomial Naive Bayes, Linear Support Vector Classifiers, and k-NN Classifiers. These approaches enable the classification of resumes based on specific criteria.

Cite this Research Publication : Jacob, Prisly Mary, Susan Jacob, Jo Cheriyan, and Lekshmi S. Nair ”ResumAI: Revolu- tionizing Automated Resume Analysis and Recommendation with Multi-Model Intelligence.” In 2023 Global Conference on Information Technologies and Communications (GCITC), pp. 1-7. IEEE, 2023.

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