Job Recommendation based on Job Profile Clustering and Job Seeker Behavior

Mhamdi, D.; Moulouki, Reda; Ghoumari, M. Y. El; Azzouazi, Mohamed; Moussaid, Laila

doi:10.1016/j.procs.2020.07.102

Cited by 23 publications

(13 citation statements)

References 5 publications

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“…Resultantly, job offers are divided into job groups or clubs based on common features among them. Jobs are matched to job finders based on their actions [ 6 ]. The authors of [ 7 ] designed and implemented a recommender system for online job searching by contrasting user and item-based collaborative filtering algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Content-Based recommendations are specific to a specific user, as the model does not use any information about other users on the page. [6] is a machine learning technique that makes decisions by taking into account the similarities in the features of the data present. Two methods used to decide are as follows:…”

Section: Data Collectionmentioning

confidence: 99%

“…Resultantly, job offers are divided into job groups or clubs based on common features among them. Jobs are matched to job finders based on their actions [6].…”

Section: Related Workmentioning

confidence: 99%

“…Content-based Filtering [ 6 ] is a machine learning technique that makes decisions by taking into account the similarities in the features of the data present. Two methods used to decide are as follows: Firstly, users provide a list of features out of which they can choose whatever they find the best.…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

“…Resultantly, job offers are divided into job groups or clubs based on common features among them. Jobs are matched to job finders based on their actions [6].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Second, most students hope to get employed as soon as possible, share the source of family income, or choose to enter national enterprises to stabilize their income [2]. Third, to obtain a university diploma, according to the needs of social employment human resources, we can obtain a professional qualification certificate, and then enter the private company and enter the entrepreneurial direction [3,4].…”

Section: Introductionmentioning

confidence: 99%

The Employment Senson System of College Students

Huang¹,

Chen²,

Chung³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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In the Internet era, we actively promote and broadcast recruitment information to college students, actively help students’ employment needs, and provide the latest job market information. The key to the success of wireless sensor systems for job hunting is to actively provide customized requirements and the latest development status of professional titles, and broadcast to students on campus or just leaving school. After strict system requirements analysis, overall model design, coding, system development tool selection, design pattern integration, testing, and completion. The research and design are to store the position information of the recruitment website in MySQL through the crawler technology in the database, and the system selects the position information according to the filing requirements. Through email, APP, and SMS, the employment information can be quickly pushed to the demanders to help teachers, students, and parents quickly screen out their expected employment fields.

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