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Piao, Guangyuan and Breslin, John G. (2016) Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations. UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. pp. 291-292.
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Abstract
The main contribution of this work is the comparison of
three user modeling strategies based on job titles, educational
fields and skills in LinkedIn profiles, for personalized MOOC
recommendations in a cold start situation. Results show
that the skill-based user modeling strategy performs best,
followed by the job- and edu-based strategies.
| Item Type: | Article |
|---|---|
| Keywords: | Analyzing; MOOC Entries; Professionals; LinkedIn; User Modeling; Personalized; MOOC Recommendations; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15639 |
| Identification Number: | 10.1145/2930238.2930264 |
| Depositing User: | Guangyuan Piao |
| Date Deposited: | 08 Mar 2022 15:23 |
| Journal or Publication Title: | UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization |
| Publisher: | Association for Computing Machinery (ACM) |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/15639 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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