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Technical Job Recommendation System Using APIs and Web Crawling.

Naresh Kumar1, Manish Gupta2, Deepak Sharma3

  • 1Department of Computer Science & Engineering, Maharaja Surajmal Institute of Technology, Janakpuri 110058, New Delhi, India.

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Summary
This summary is machine-generated.

Job seekers can now find relevant engineering roles faster. This automated system uses web crawling and hybrid filtering to recommend quality job openings, improving the job search process.

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Area of Science:

  • Computer Science
  • Information Technology
  • Software Engineering

Background:

  • The rapid growth of the tech industry and startups presents challenges for job seekers.
  • Manually tracking job openings across numerous company career portals is time-consuming and leads to missed opportunities.

Purpose of the Study:

  • To automate the process of identifying and recommending relevant job openings in the engineering domain.
  • To simplify job searching for individuals in the technical sector.

Main Methods:

  • Web crawling using Puppeteer and Representational State Transfer (REST) APIs.
  • Implementation of a hybrid recommendation system combining Content-Based Filtering and Collaborative Filtering.

Main Results:

  • The system successfully aggregates and recommends appropriate job opportunities.
  • Testing on various use cases with a web application interface demonstrated satisfactory performance.
  • The proposed system outperformed existing job recommendation solutions.

Conclusions:

  • The developed automated system effectively addresses the challenges of job searching in the booming tech industry.
  • The hybrid recommendation approach prioritizes quality job matches over quantity, enhancing the user experience.