Career path recommendation system for UiTM Perlis students using fuzzy logic

Razak, Tajul Rosli; Hashim, Muhamad Arif; Noor, Noorfaizalfarid Mohd; Halim, Iman Hazwam Abd; Shamsul, Nur Fatin Farihin

doi:10.1109/icias.2014.6869553

Cited by 53 publications

(28 citation statements)

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“…At present, career centers of institutions and colleges mainly provide manual job recommendation to graduates based on information from questionnaire surveys (Al-Otaibi and Ykhlef 2012; Nguyen et al 2013). Other graduate job recommendation systems are implemented based on the similarity calculation (Almalis et al 2015;Liu et al 2017;Patel and Kakuste 2017;Razak et al 2014). For example, Patel and Kakuste (2017), Razak et al (2014) have proposed a job recommendation system, which match the similarity between the skills extracted from a resume and the skills required by a job.…”

Section: Graduate Job Recommendationmentioning

confidence: 99%

“…To address this problem, some studies recommend jobs based on the similarity between the skills extracted from a resume and the skills required by a job position (Al-Otaibi and Ykhlef 2012; Patel and Kakuste 2017;Razak et al 2014). The accuracy of this method relies on the objectivity of skills described in the resume, but it is difficult to achieve 1 3 because a resume is essentially a self-report.…”

Section: Introductionmentioning

confidence: 99%

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Job recommendation algorithm for graduates based on personalized preference

Zhou

Liao

Chen

et al. 2019

CCF Trans. Pervasive Comp. Interact.

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It is challenging for graduates to find a proper job. Unlike those with occupational history, graduates generally are short of work experience and the support from social network, so they have to face hundreds of recruitment companies. The process of applying for a job is time-consuming, especially in preparing and attending tests and interviews. Not knowing which companies are most proper for them, graduates need to devote their energy and time to preparing for each potential recruitment. This job-hunting strategy can easily lead to employment dissatisfaction or failure. Therefore, it is very helpful to recommend a few most suitable jobs to graduates. Collaborative filtering (CF) method is currently the most frequently adopted and effective recommendation algorithm, but it cannot be directly applied to job recommendation for graduates because graduates generally have no historical records on employment. Besides, job recommendation should take into account graduate preferences for jobs, such as enterprise types and company locations, which are crucial to job choices. To address these challenges, we first analyze the pattern of job choices of graduates. Based on this, we propose a personalized preference collaborative filtering recommendation algorithm (P2CF), which can not only recommend jobs for graduates through massive campus records, but also identify graduate personal preferences for jobs. Graduates are first clustered into different groups according to their academic performances and family economic conditions. Then Bayesian personalized ranking (BPR) method is introduced to calculate the scores of graduate groups to jobs. Finally the scores and graduate personalized preferences are combined to recommend a few potential jobs. P2CF is a recommendation algorithm with hierarchical structure, which takes account of both the group records of job choices and the individual preferences for jobs. Experimental results show that P2CF on job recommendation outperforms state-of-the-art CF methods and identifies graduate personalized preference for jobs accurately.

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Section: Graduate Job Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Job recommendation algorithm for graduates based on personalized preference

Zhou

Liao

Chen

et al. 2019

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

show abstract

“…We employed a Fuzzy similarity algorithm. Fuzzy similarity [36][37][38]41] measures similarity between item and is given by…”

Section: Memory-based Cfmentioning

confidence: 99%

Occupation recommendation with major programs for adolescents

Ochirbat

Shih²

2017

Proceedings of International Symposium on Grids and Clouds (ISGC) 2017 — PoS(ISGC2017)

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Choosing a major in high school or undergraduate stage is an important decision in the person life. To find students' majors as earlier as possible can help them to choose correct learning direction. Hence, it is essential to build a recommendation system that provides direction and guidance to students. In this regard, this study proposes Occupation Recommendation System with major program of study (ORS), a framework to assist students in the major/occupation selection, to provide the information about contemporary occupations, and to suggest the suitable occupations with major program of study based on their learning style, personality and vocational interests. Collaborative Filtering (CF) methods are employed, namely, a memory-based CF on vocational interest and a model-based CF on a regression model according to the learning style and the personality. The system provide top 10 occupations for each method respectively. This paper describes the architecture and interface of ORS and shows the experiments with 190 high school students. Moreover, comparisons between recommended majors, suggested majors by parents and students' intended majors are made.

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“…The system output was determined with respect to the four selected professional fields; therefore, a more comprehensive study could be applied in the future to all other professional fields. Tajul R., et al (2014) in [13] proposed a career recommendation system using a fuzzy logic technique. The study has explained that the use of fuzzy logic helps students by giving career recommendations based on career tests.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Implementing Cloud-based Career and Educational Guidance System using Fuzzy Logic Modelling

El-Sofany¹

2020

IJEAT

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The career guidance process for graduates and students is affected by many factors; this, in turn, has motivated researchers to use a variety of scientific methodologies and techniques for proposing career guidance systems and solving related problems. The choice of right career not only positively affects the professional life of graduates, but also the academic life of students. As a result, the significance of developing career and educational guidance systems has increased. In this paper, the researcher discusses the effectiveness of using the proposed cloud-based career and educational guidance system to help students and graduates move to the professional world. The main objectives of the system include helping students choose their majors; helping graduates choose a career that is appropriate to their educational skills, practical experiences, and scientific ability; providing graduates and students with training courses required for specific careers. The proposed system is presented as a “Career-as-a-Service” cloud model. In this paper, the use of Fuzzy Logic for defining system inputs, processes, and outputs as a new representation for career and educational guidance system parameters is introduced. Cronbach’s alpha tests are used for measuring the validity and reliability of the study questionnaires’ content. In this study, several analysis methods such as Spearman correlation, stepwise multiple linear regression, skewness, mean, and standard deviation have been used to determine the effect and performance of the proposed system through dominating factors such as gender, age, class standing, enrolment status, specialization, and city.

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Career path recommendation system for UiTM Perlis students using fuzzy logic

Cited by 53 publications

References 1 publication

Job recommendation algorithm for graduates based on personalized preference

Job recommendation algorithm for graduates based on personalized preference

Occupation recommendation with major programs for adolescents

Assessment of Implementing Cloud-based Career and Educational Guidance System using Fuzzy Logic Modelling

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