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Indigenous Pre Recruitment Course 2016
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Received: April 12, 2022 / Revised: May 26, 2022 / Accepted: June 10, 2022 / Published: June 17, 2022
With millions of students/employees browsing course information and job adverts every day, the need for an accurate, efficient, meaningful and transparent course and job recommendation system is more evident than ever. Current recommendation research has attracted considerable attention in academia and industry. However, existing studies have mainly focused on content analysis and deconstructing user characteristics of courses or jobs, and have failed to explore cross-domain data integration between careers and education. At the same time, not fully exploiting the relationship between training, skills, and jobs helps improve the accuracy of recommendations. Therefore, this study aims to propose a new cross-domain recommendation model to help students/employees search for suitable courses and jobs. Using heterogeneous graphs and community detection algorithms, this study presents a Graph-Community-Enabled (GCE) model that integrates data from course profiles and recruitment data. Specifically, to bridge skill gaps between occupations and curricula, skill groups calculated by community detection algorithms are used to link curriculum and job information. Then, an innovative heterogeneous graph method and a random walk algorithm enable cross-domain information recommendations. The proposed model is evaluated on real-world job datasets from recruitment websites, MOOCs, and higher education training datasets. Experiments show that the model is clearly superior to the classical baseline. The approach described can be replicated in a variety of educational/career contexts.
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Education; career; heterogeneous data / heterogeneous graph mining; information recommendations; cross-domain education; career; heterogeneous data / heterogeneous graph mining; information recommendations; throughout the domain
What are the benefits of education for career planning? Finding a fulfilling career is a convenient and obvious popular answer. Student/employee productivity, employability, and career satisfaction are enhanced through lifelong learning, or the acquisition of knowledge to meet personal and professional goals. Students/employees are constantly exploring various educational opportunities to enhance their knowledge in order to achieve their career goals. Education should generally support the ecological employment system , while reducing the skills gap between academia, school and industry .
With the rapid development of internet technology, online resources have made it easier for students/employees to find educational and job related information. At the same time, it brings with it the problem of “information overload,” confusing students and employers with the vast amount of online material that prevents them from quickly identifying the courses and jobs that are best for them. Recommender systems that help users find the most relevant items are a promising way to filter information. It will provide a variety of specialized recommendations based on each user’s personal needs and preferences.
Recommender systems in education and employment can help students/employees make better and more informed decisions, thereby influencing their future. Many classic job/career and curriculum/educational recommendation (JCR) systems have been proposed, including the CourseAgent system , the CourseRank system  and the CaPaR framework . Although they are compliant, existing JCRs have individual student/staff requirements and limited impact. Furthermore, most algorithms in previous studies have used user-based modeling (UBM), content-based modeling (CBM) and collaborative filtering (CF). While UBM focuses on analyzing learner or job applicant profiles, CBM focuses on examining course or job content features, and CF primarily examines course evaluations, user learning history, and employment history. However, important implicit information that helps to increase the validity of the proposition, such as links between courses, skills and jobs, is not adequately exploited [6, 7]. More sophisticated routes such as job-skills-skills-course-course can be used to directly link courses and jobs. According to this view, the links between courses, skills and jobs may be much more complex than traditional CF approaches suggest. Furthermore, numerous studies have been conducted on course recommendations and job recommendations , but cross-domain data integration between the two has rarely been considered.
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Therefore, this study proposes a new Graph-Community Enabled (GCE) method to solve the cross-domain career education recommendation problem from a heterogeneous graph mining perspective. The two domains have three different nodes that interact through four types of relationships. Therefore, the recommendation policy is changed to a graph-based random walk policy. Figure 1 shows the integration of two different information sources, education and career, into a heterogeneous network using skills as a bridge. However, there is a difference between job skills and course skills because different vocabularies are used. We use the Infomap algorithm to calculate skill groups, which helps link job and course data. Finally, five meta-pathway features were manually constructed for recommendation based on the appropriate indexed heterogeneous graphs. Ranking predictions are expressed for each feature. After that, using a random walk algorithm, we can deliver courses and job offers that are more tailored to your future career aspirations or intended educational level.
The significance and uniqueness of this research lies in the aggregation and indexing of work and education data through multiple networks and community discovery to achieve cross-domain information recommendations. Experiments were conducted using MOOC and university course data as well as job postings in the IT industry to demonstrate how students/employees can benefit from graph-based data integration. Findings suggest that the strategy is effective in making curriculum recommendations based on predetermined career goals. The proposed method can be used in various educational and professional settings.
We note that an earlier version of this paper was presented at an international conference  and is available on arXiv (https://arxiv.org/, accessed 11 April 2022) . Our previous conference paper conducted only simple preliminary tests on the course recommendation task and did not fully validate the proposed model. This manuscript aims to improve the connectivity quality of heterogeneous graphs by combining Word2Vec/Bert technology with community detection algorithms to solve the skill gap problem in training and work contexts. In addition, several different meta-tracks have been developed for course and work recommendation tasks according to different types of user application scenarios, using several data (ie, MOOCs added) and several basic methods to analyze and verify effectiveness. of the proposed model for enriching test content. More details are provided in Section 3.
The structure of this paper is as follows: Section 2 reviews the relevant literature; Chapter 3 describes the data collection process and the proposed community-based graphical method; Section 4 discusses the results and provides conclusions and suggestions for future work.
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Recommender systems have been widely used in lesson planning [6, 9, 10]. In most of these studies, courses were recommended to end users based on feedback from other users [ 11 , 12 ], overall user performance [ 13 , 14 ] or similarity of course content [ 15 , 16 ]. For example, Nguyen et al.  used a sequential rule to find the optimal subject pair and grade. Recurrent Neural Networks were used by Morsi and Karipis  to suggest lessons to help students maintain or improve their GPA. In general, training recommendation systems rarely consider the target profession [1, 19].
Similarly, job recommendation systems have attracted much attention from the academic community in recent decades. Some studies have examined job recommendations related to career orientation. Paparrizos et al.  trained machine learning models that use previous job experiences to predict candidates’ next job transitions. Patel et al.  presented a CaPaR or “career path recommendation” framework that explores users’ work experiences using text mining and collaborative filtering methods.
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