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Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
- Source :
- Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track ISBN: 9783030676667, ECML/PKDD (4)
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines.
- Subjects :
- Computer science
business.industry
02 engineering and technology
010501 environmental sciences
Machine learning
computer.software_genre
01 natural sciences
ComputingMethodologies_PATTERNRECOGNITION
Ranking
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Optimal combination
Reinforcement learning
Artificial intelligence
business
computer
0105 earth and related environmental sciences
Subjects
Details
- ISBN :
- 978-3-030-67666-7
- ISBNs :
- 9783030676667
- Database :
- OpenAIRE
- Journal :
- Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track ISBN: 9783030676667, ECML/PKDD (4)
- Accession number :
- edsair.doi...........9587a5344a9329d920748efbdb5ec353