Technical Job Recommendation System Using APIs and Web Crawling

There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this res...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-06, Vol.2022, p.1-11
Main Authors: Kumar, Naresh, Gupta, Manish, Sharma, Deepak, Ofori, Isaac
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Automation
Candidates
Collaboration
Data collection
Employment
Filtration
Hybrid systems
Methods
Ontology
Recommender systems
Scientific papers
Semantics
User behavior
Websites
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Summary:There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/7797548