RésuMatcher: A personalized résumé-job matching system

Guo, Shiqiang; Alamudun, Folami; Hammond, Tracy

doi:10.1016/j.eswa.2016.04.013

Cited by 75 publications

(34 citation statements)

References 23 publications

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“…We adopt the idea presented by Guo, Alamudun, and Hammond (2016). If the major in undergraduate's profile and research project's major requirement are the same, the major qualification score is 1.…”

Section: Requirement Filtering Modulementioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

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Academic collaboration plays an important role in undergraduate research. Current methods rely on offline social contacts for undergraduate students to collaborate with academic staff members in universities and research institutions. In big data era, it is difficult for undergraduate students to find suitable research project opportunities and supervisors to work with. This paper proposes a social recommendation system for undergraduate students to find research project opportunities and work with research project teams on an academic collaboration network. The proposed recommendation method integrates relevance, connectivity, and quality modules, where profiles of undergraduates are constructed with their self‐claimed information, research activities (e.g., studying and reading research publications and reading research projects), and social connections in the academic collaboration network. Suitable research projects are recommended based on the undergraduates' profiles. Experiments are conducted, and the results have shown that the proposed social recommendation system can facilitate undergraduates' selection of research projects.

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Section: Requirement Filtering Modulementioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

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“…Information extraction from resumes and job descriptions is one of the main areas of research in jobs and recruitment industry [26]- [28]. These works involve text mining, skill normalization, and developing similarity metrics for matching jobs and candidate profiles.…”

Section: B Job Recommendation Systemsmentioning

confidence: 99%

“…Most of the existing recommendation systems in this domain focus on candidate selection by human resources rather than attracting job seekers through job recommendations which is the main goal of our paper [29], [30]. Existing automated job recommendation systems belong to either content-based applicant-job matcher approaches [28], [31], or user-based methods [32], [33].…”

Section: B Job Recommendation Systemsmentioning

confidence: 99%

Help me find a job: A graph-based approach for job recommendation at scale

Shalaby

AlAila

Korayem

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

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Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever. While recommendation systems are successfully advancing in variety of online domains by creating social and commercial value, the job recommendation domain is less explored. Existing systems are mostly focused on content analysis of resumes and job descriptions, relying heavily on the accuracy and coverage of the semantic analysis and modeling of the content in which case, they end up usually suffering from rigidity and the lack of implicit semantic relations that are uncovered from users' behavior and could be captured by Collaborative Filtering (CF) methods. Few works which utilize CF do not address the scalability challenges of real-world systems and the problem of cold-start. In this paper, we propose a scalable item-based recommendation system for online job recommendations. Our approach overcomes the major challenges of sparsity and scalability by leveraging a directed graph of jobs connected by multi-edges representing various behavioral and contextual similarity signals. The short lived nature of the items (jobs) in the system and the rapid rate in which new users and jobs enter the system make the cold-start a serious problem hindering CF methods. We address this problem by harnessing the power of deep learning in addition to user behavior to serve hybrid recommendations. Our technique has been leveraged by CareerBuilder.com which is one of the largest job boards in the world to generate high-quality recommendations for millions of users.

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“…job recommender systems to help match the right candidate with the right job. Examples include CASPER [1], Proactive [2], FES [3], PROSPECT [4], eRecruiter [5], iHR [6], RésuMatcher [7] and the work of [8]. The work of [9] and [10] provides comprehensive review on job recommender systems.…”

Section: Introductionmentioning

confidence: 99%

A Map-based Job Recommender Model

Alghieth¹,

Shargabi²

2019

IJACSA

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Location is one of the most important factors to consider when looking for offering a new job. Currently, there exist many job recommender systems to help match the right candidate with the right job. A review of the existing recommender systems, included within this article, reveals that there is an absence of appropriate mapping support offering for job recommendation. This article aims to propose a general mapbased job recommender model, which is implemented and applied within a system for job seekers in Saudi Arabia. The system adapts content-based technique to recommend jobs using the cosine similarity and will help Saudi job seekers finding their desired job in an efficient way using interactive maps. This ultimately will contribute to Saudi Arabia moving forward to the digital transformation which is one of the major objectives to fulfill the Saudi vision 2030.

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RésuMatcher: A personalized résumé-job matching system

Cited by 75 publications

References 23 publications

A social recommendation system for academic collaboration in undergraduate research

A social recommendation system for academic collaboration in undergraduate research

Help me find a job: A graph-based approach for job recommendation at scale

A Map-based Job Recommender Model

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