High-level overview of the process of Resume and JD shortlistlisting with OpenAI
Data Collection: Collect job descriptions from various sources such as online job boards, company websites, and job aggregators. Also, collect resumes from users who want to compare their resumes to job descriptions.
Text Processing: Clean and preprocess the collected job descriptions and resumes. This includes removing unnecessary characters, normalizing the text, and tokenizing the documents into smaller units (words, sentences, etc.) for further processing.
Model Training: Train a machine learning model using the preprocessed job descriptions and resumes. One approach is to use Natural Language Processing (NLP) techniques like word embeddings or transformers (e.g., BERT) to create document embeddings for job descriptions and resumes. The model should be trained to calculate similarity or match scores between job descriptions and resumes.
Application Development:
Frontend Development: Design and develop a user-friendly interface where users can input their resumes and job descriptions. This can be implemented using web technologies such as HTML, CSS, and JavaScript.
Backend Development: Develop the backend of the application using a web framework like Django or Flask. This backend will handle user requests, process the data, and communicate with the machine learning model for text comparison.
Text Comparison: Pass the user's resume and job description through the trained machine learning model to calculate the similarity score. The model should compare the user's resume with multiple job descriptions and rank them based on similarity.
Shortlisting: Based on the similarity scores, shortlist the job descriptions that match the user's resume to a certain threshold. For example, you can set a threshold score, and only job descriptions with scores above that threshold will be considered as matches.
Display: Display the shortlisted job descriptions to the user along with the similarity scores. Provide additional details about each job, such as job title, company name, location, and any other relevant information.
User Engagement: Implement additional features to engage users, such as the ability to save or apply for jobs directly from the application, providing personalized recommendations, and tracking job application progress.