IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i12p7439-d841496.html
   My bibliography  Save this article

Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

Author

Listed:
  • Guoqing Zhu

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Yan Chen

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Shutian Wang

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

Abstract

With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident than ever. The current recommendation research has attracted wide attention in the academic and industrial areas. However, existing studies primarily focus on content analysis and user feature extraction of courses or jobs and fail to investigate the problem of cross-domain data integration between career and education. At the same time, it also fails to fully utilize the relations between courses, skills, and jobs, which helps to improve the accuracy of the recommendation. Therefore, this study aims to propose a novel cross-domain recommendation model that can help students/employees search for suitable courses and jobs. Employing a heterogeneous graph and community detection algorithm, this study presents the Graph-Community-Enabled (GCE) model that merges course profiles and recruiting information data. Specifically, to address the skill difference between occupation and curriculum, the skill community calculated by the community detection algorithm is used to connect curriculum and job information. Then, the innovative heterogeneous graph approach and the random walk algorithm enable cross-domain information recommendation. The proposed model is evaluated on real job datasets from recruitment websites and the course datasets from MOOCs and higher education. Experiments show that the model is obviously superior to the classical baselines. The approach described can be replicated in a variety of education/career situations.

Suggested Citation

  • Guoqing Zhu & Yan Chen & Shutian Wang, 2022. "Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7439-:d:841496
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/12/7439/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/12/7439/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xueying Ma & Lu Ye, 2018. "Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan," International Journal of Mobile and Blended Learning (IJMBL), IGI Global, vol. 10(3), pages 23-37, July.
    2. Einat Minkov & Keren Kahanov & Tsvi Kuflik, 2017. "Graph-based recommendation integrating rating history and domain knowledge: Application to on-site guidance of museum visitors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1911-1924, August.
    3. Ahood Almaleh & Muhammad Ahtisham Aslam & Kawther Saeedi & Naif Radi Aljohani, 2019. "Align My Curriculum: A Framework to Bridge the Gap between Acquired University Curriculum and Required Market Skills," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Monica Mihaela Maer Matei & Anamaria Beatrice Aldea, 2019. "Employers’ Requirements for Data Scientists - an Analysis of Job Posts," Logos Universalitate Mentalitate Educatie Noutate - Sectiunea Stiinte Economice si Administrative/ Logos Universality Mentality Education Novelty - Section: Economical and Administrative Sciences, Editura Lumen, Department of Economics, vol. 4(1), pages 21-32, December.
    2. Spada, Irene & Chiarello, Filippo & Barandoni, Simone & Ruggi, Gianluca & Martini, Antonella & Fantoni, Gualtiero, 2022. "Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7439-:d:841496. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.