Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Autor: Ibon Oleagordia-Ruiz, María Cora Urdaneta-Ponte, Amaia Mendez-Zorrilla
Jazyk: angličtina
Rok vydání: 2021
Předmět:
DBSCAN
hybrid system recommendation
Technology
Computer science
Process (engineering)
QH301-705.5
QC1-999
Lifelong learning
02 engineering and technology
Ontology (information science)
Recommender system
Machine learning
computer.software_genre
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
ontology
Biology (General)
Cluster analysis
Instrumentation
QD1-999
Fluid Flow and Transfer Processes
Serendipity
business.industry
Process Chemistry and Technology
Physics
05 social sciences
General Engineering
050301 education
Engineering (General). Civil engineering (General)
lifelong learning courses
Computer Science Applications
Chemistry
machine learning
Artificial intelligence
TA1-2040
Heuristics
business
0503 education
computer
Zdroj: Applied Sciences, Vol 11, Iss 3839, p 3839 (2021)
Applied Sciences
Volume 11
Issue 9
ISSN: 2076-3417
Popis: Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system.
Databáze: OpenAIRE
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