A New Content-Based Recommendation Algorithm for Job Recruiting

Almalis, Nikolaos D.; Tsihrintzis, George A.; Karagiannis, Nikolaos

doi:10.1007/978-3-319-25032-8_32

Cited by 4 publications

(1 citation statement)

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“…(v) e proposed system consists of the following three major modules, which are completed as part of this research as follows: (vi) Data collection and preprocessing [19,20] followed by the unification of the database. (vii) Recommendation of suitable results using a hybrid system of content-based [21][22][23] and collaborative filtering. (viii) Development of a fully functional user interface in the form of a web application.…”

Section: Proposed Systemmentioning

confidence: 99%

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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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.

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Section: Proposed Systemmentioning

confidence: 99%