Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning
2. Related Studies
3. Materials and Methods
3.2.1. Off-Line Phase
3.2.2. On-Line Phase
Taxonomic Similarity for Job Sectors
- Job sectors.
- : Depth of taxonomy.
- : Constant for relevance of level l in the taxonomy.
- : Level l function comparison, with wildcard support and
Taxonomic Similarity for Areas of Knowledge
Similarity of User Skills
Assessment of Skill Development
- : Skill set i.
- : Skill set j.
- : Cut-off threshold for the taxonomic similarity function.
Cosine Similarity Function for User Skills
Pearson’s Similarity Function for User Skills
Determining Related Job Sectors (2)
- , job sectors where users have the skill recorded through events in the ontology.
- predicts the cluster for a job sector according to clustering by ML in the ontology.
- , the previous sectors in to according to the orderly relationship given by the hierarchy in the ontology.
- are the skills represented in the ontology, which are the product of an event-driven update of user profiles.
- are the skills of interest associated with the skills represented in the ontology.
- , function that verifies that the skill is valuable for the skills .
- constructs a set with the most frequent elements in the multiset .
- , Multiset of related skills.
Initial Course Prediction (3)
Filtering Heuristics and Course Ordering (4)
- Users possessed the necessary skills to take the courses; otherwise, courses were selected to develop those skills, applying demographic restrictions.
- User demographic restrictions were applied to the initial course prediction, and to the result of the previous phase.
- In the prediction of courses, courses where the skills to be developed were the same or a subset of another were deleted, and the best-rated prediction was kept.
Measurements for the Evaluation of RSs
- Content filtering using user’s own and associated skills; no related skills were determined.
- Collaborative filtering using only user’s own job sectors; semantically related skills were determined.
- Semantic filtering using rules to determine related job sectors and related skills.
- Semantic filtering using 75% coverage of user skills to determine related job sectors, i.e., those job sectors that covered 75% of the user’s skills were selected and used to determine related skills.
- Semantic filtering using 50% coverage of user skills to determine related job sectors, i.e., those job sectors that covered 50% of the user’s skills were selected and used to determine related skills.
- Semantic filtering using DBSCAN clustering to determine related job sectors and semantic rules to establish related skills.
- Semantic filtering using K-Means clustering to determine related job sectors and semantic rules to establish related skills.
- Semantic filtering using DBSCAN and K-Means clustering to determine related job sectors and semantic rules for related skills using the predicted job sectors with both clusters, and related skills were determined based on both clusters.
5. Discussion and Conclusions
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Not provided||Not provided|
|||2015||Social media recommender systems for the development of social media skills||Ontology|
Filtering by content and context
|Social media||Quality of results and integration of functions by users||3.62/5|
|||2017||Educational resources recomendations||Ontology|
Sequential pattern mining (SPM)
|Comparison of user satisfaction between the proposed system and other RSs, without ontology, and without SPM.||94%|
|||2017||Recommendation of massive learning activities||OntologyKnowledge-based filtering||Students’|
|Not provided||Not provided|
|||2017||Recommendation of learning materials||Ontology|
|Educational Material||Comparison with other algorithm||Improved precision by 2.8|
|||2018||Job recommendations||Ontology||Skills||Calculating similarity scores of Pointwise Mutual Information (PMI)||0.975|
|||2018||Career path recommendation||Ontology||Skills||Calculation of information recall related parameters||Precision: 80.54%|
|||2019||Recommending the right degree and university for each person||Ontology|
|Higher education institution.|
|||2020||Recommendation of e-learning resources||Ontology|
|Measurement||Description||Calculation for the User||Calculation for System|
|MAE||Mean absolute error of the given recommendation vs. the recommendation expected by the user|
|RMSE||Root mean square error|
|Coverage||Users to whom the system has made a recommendation, CRi set of recommendations from ui ∈U|
|Precision:||The fraction of the recommendation that is relevant to the user.|
|Recall:||Calculates the ratio between recommendation and user preference, where CEi is the preference group of ui ∈U|
|Novelty||The portion of recommendations made to the user that the user is not familiar with or has not seen before|
|Serendipity||The fraction of the recommendation that is unexpected and valuable to the user.|
|Setting||Test 1||Test 2||Test 3|
|Type||Hybrid RS||Hybrid RS||Hybrid RS||Hybrid RS||Hybrid RS|
|Items to be recommended||MOOC||MOOC||University courses||Lifelong learning||Lifelong learning|
|Use of social network||Yes||-||No||Yes||Yes|
|Types of data||-||-||Structured||Semi-structured/|
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Urdaneta-Ponte, M.C.; Méndez-Zorrilla, A.; Oleagordia-Ruiz, I. Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning. Appl. Sci. 2021, 11, 3839. https://doi.org/10.3390/app11093839
Urdaneta-Ponte MC, Méndez-Zorrilla A, Oleagordia-Ruiz I. Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning. Applied Sciences. 2021; 11(9):3839. https://doi.org/10.3390/app11093839Chicago/Turabian Style
Urdaneta-Ponte, María Cora, Amaia Méndez-Zorrilla, and Ibon Oleagordia-Ruiz. 2021. "Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning" Applied Sciences 11, no. 9: 3839. https://doi.org/10.3390/app11093839