Taxonomy-Based Hybrid Recommendation System for Lifelong Learning to Improve Professional Skills

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

“…The study presented by [11] proposed a hybrid RS for recommending lifelong learning courses aimed at improving and developing professional competences based on information on the employment sector and skills of LinkedIn users. A taxonomy code was used to classify skills by area of knowledge, taking into account the level of specialisation and degree of updating, which meant that more efficient comparisons could be made, and similarities could be calculated, in order to offer a better recommendation.…”

“…Based on this review and on the advances obtained in [11], it is proposed to use an ontology to enrich the existing taxonomies in order to model the knowledge related to the users' skills and work sectors to optimise performance and optimise the prediction of the recommendation system for lifelong learning courses. Using profiling data from professional records in social media, the skills used in different positions and professional practice (job sectors) were determined.…”

“…In the era of Big Data, it is challenging to find relationships and patterns from within vast amounts of mostly unstructured or semi-structured information. In this context, an important role is played by recommendation systems (RSs), which are mainly aimed at predicting user ratings or preferences of an item that are valuable to the user [11]. Examples of popular recommendation systems include Amazon, especially in the commerce sector; Facebook, that can recommend social connections and Netflix, which offers movie recommendations based on user preferences and viewing history [12].…”

“…The use of semantic web techniques in education has also brought some improvements such as personalised content, recommendation of educational resources for students and the collection of data regarding students' interaction with educational environments [16]. This means that people can be matched to the educational offering available, which enables them to improve their knowledge and professional skills and help them in their decision making [11]. In addition, semantic web techniques have been combined with machine learning (ML) techniques.…”

“…The courses are selected from the course catalogue previously selected from the web, related to the user's job sectors, whose content increases the level of specialization and/or the degree to which the user's skills are current. This proposal is presented as a development of a hybrid recommendation system based on taxonomies and collaborative filtering [11] to test the use of ontology and ML in improving the performance and precision of RSs.…”