AI Based Job Recommedation System using BERT

Rapid growth in the technical sector has led to an increase in profitable startups. However, it has grown more difficult to keep up with the abundance of suitable job openings at leading industry firms. Due to this, important deadlines and career possibilities may be missed. In order to give users more accurate and customized job recommendations, this research paper addresses the advantages of using web scraping tools and machine learning algorithms in job recommendation systems. The proposed job recommendation system combines web scraping and natural language processing (NLP) algorithms. It extracts job details from a well-known Indian job portal for IT jobs using a customized web scraping service, preprocesses the data, and then uses the BERT model to create embeddings for job and user profiles. Based on user input, the system creates ten job recommendations and embeds job descriptions and relevant keywords using the Universal Sentence Encoder. The benefits of implementing such a system are highlighted in the study, including improved job recommendation accuracy and the capacity to customize work recommendations based on user profiles.