Abstract
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.
Highlights
In this paper we present a proposal that combines ontology with machine learning (ML) techniques, 5
Discussion and Conclusions aimed at grouping related jobs according to the similarity of the skills they use, to make lifelongInlearning recommendations basedcombines on their skills and job sectors, this paper we presentto aprofessionals proposal that ontology with ML tec current and related ones
Aimed at grouping related jobs according to the similarity of the skills they use, The system architecture made it possible to use different configurations and to evaluate lifelong learning recommendations to professionals based on their skills and job each of the functionalities separately and, if necessary, several of them simultaneously, current anduse related ones. frequency
Summary
Organisations are currently undergoing significant changes as evidenced by the continuous and rapid transformation in response to globalisation and to advances in information and communication technologies. According to study [1], employability requirements have changed to address these new situations and challenges, and discrepancies have emerged in some areas between the employees’ skills and labour market needs. This has revealed that the knowledge acquired by graduates in a higher education course of study is outdated by the time they obtain their diplomas
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