Empowering Job Seekers by a Natural Language Solution for Personalized Job Descriptions and Enhanced Employment Prospects through Clustering Techniques

This Paper is based on our project which aims at implementing a Natural Language based solution which would deliver job descriptions based on a few criteria like preparing the transformed dataset, performing NLP processing to rank the best features in the Job Description, training transformer model and implementing an API endpoint which can be consumed over the web. The input data has been refined and data dictionaries have been created to generate Job description based on job title and skills. To anticipate the token that should come after the input, we have trained the GPT2 decoder transformer model to use the most recent token embedding of the input sequence. The study concludes that it is critical for job searchers to customize job descriptions according to an individual's talents, prior experiences, and educational background. While most of the research to date has focused on the client side of the hiring process, the suggested model seeks to empower job searchers by giving them customized job descriptions, which increases the likelihood that they will find acceptable employment. To provide insights into a variety of disciplines and help job searchers better grasp the dynamics of the recruiting process, the initiative made use of an extensive dataset. K-means clustering and DBScan clustering were two of the models and hyperparameters investigated within the project's time restrictions. K-means clustering improved context knowledge, albeit at the expense of adding noise to the data. Subsequent developments may entail investigating topic modeling and clustering strategies in greater detail to strengthen the model. Prospective directions for further research encompass utilizing Part of Speech (POS) tagging to extract distinct portions of job descriptions, implementing machine learning techniques to enhance synonym lists, and investigating substitute text creation models if preliminary efforts provide inconclusive results. With these developments, the suggested model is continuously improved and optimized for better accuracy and performance.