VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/1758
Title: | A study into machine learning algorithms: a GPT-powered virtual interviewer application | Authors: | Nguyen Anh Tai | Keywords: | Machine learning;GPT-powered;Virtual interviewer application | Issue Date: | 2023 | Abstract: | n today's highly competitive job market, preparation for job interviews is critical for candidates in the marketing field. However, acquiring relevant and actual interview questions that accurately reflect the candidate's specific job description, company name, and role can be a daunting task. Additionally, receiving precise and informative feedback after an interview can be equally challenging, particularly for those at the intern to manager level. Therefore, this thesis proposes an application that addresses these challenges by providing a platform where candidates can practice interview questions that are tailored to their job description, company name, and role. The proposed application also provides precise and informative feedback on the candidate's performance during the interview. The developed application utilizes speech-to-text technologies to create a more realistic interview experience and takes into account the candidate's sentiment during the interview. This feature results in a well-rounded evaluation and recommendation for improvement. The proposed application is designed to be user-friendly, easy to navigate, and offers a convenient way for candidates to practice and improve their interview skills. The system's ability to provide tailored feedback to the candidate based on their specific job description and company name ensures that the candidate receives the most relevant and useful feedback possible |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1758 |
Appears in Collections: | Computer Science (CS) |
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File | Description | Size | Format | Existing users please Login |
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A study into machine learning algorithms a GPT-powered virtual interviewer application.pdf | 5.07 MB | Adobe PDF |
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