Feature request Resume Parser and Job description comparision usind DJL and NLP in java Programming language

[Damini2004]

Is your feature request related to a problem? Please describe.
The feature request is related to the problem of manually matching job seeker resumes with relevant job descriptions, which is a time-consuming and often inefficient process. By using a Resume Parser and Job Description Comparison feature, recruiters and hiring managers can automate and improve the accuracy of this process, saving time and improving the overall candidate experience.

Describe the solution you'd like
The solution would include the following components:

Resume Parser: A module that uses NLP techniques to extract relevant information from resumes, such as contact information, work experience, education, skills, and certifications.
Job Description Parser: A module that uses NLP techniques to extract relevant information from job descriptions, such as job title, required skills, experience, and education.
Comparison Engine: A module that compares the parsed resume data with the parsed job description data to identify the best matches. This module would use techniques such as semantic similarity measurement, keyword matching, and machine learning algorithms to rank the resumes based on their relevance to the job description.
Integration with existing systems: The feature should be easily integrable with existing Applicant Tracking Systems (ATS) or Human Resource Management Systems (HRMS) using APIs or other integration methods.
User Interface: A user-friendly interface that allows recruiters and hiring managers to easily view and manage the matches, as well as customize the comparison criteria and weights.

Describe alternatives you've considered
Utilizing third-party Resume Parsing and Matching services, such as Sovren, DaXtra, or HireAbility. While these services can provide accurate results, they may not offer the same level of customization and integration as an in-house solution.
Building the solution using different NLP libraries or frameworks, such as Stanford CoreNLP, OpenNLP, or SpaCy. While these libraries offer powerful NLP capabilities, they may not be as optimized for Java or have the same level of community support as DJL.
Implementing the solution using different programming languages, such as Python or JavaScript. While these languages have strong NLP libraries and frameworks, Java was chosen for its enterprise-level maturity, scalability, and integration capabilities.
Approach to be followed (optional)
A clear and concise description of the approach to be followed.

Additional context
The target audience for this feature would be recruiters and hiring managers in organizations of various sizes, ranging from small businesses to large enterprises. The feature would help them automate the resume screening process, reduce manual effort, and improve the quality of hire.

The feature would be built using Deep Java Library (DJL) for its ease of use, performance, and compatibility with Java. Additionally, DJL offers pre-trained models for various NLP tasks, which can help accelerate the development process.

The solution would be designed with scalability and performance in mind, ensuring it can handle large volumes of resumes and job descriptions efficiently. It would also prioritize data privacy and security, ensuring that all personal data is processed and stored in compliance with relevant regulations, such as GDPR and CCPA.

Kindly assign this issue to me under GSSOC24 with level.

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