## 1 Introduction
This is due to the type of information in the work domain they share, such as job, education and skills; characteristics to take into account when proposing a learning pathway to improve and/or develop professional skills. Users need to prioritise filtered data to efficiently access the right information on the web. 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]. RSs handle a large amount of data which, depending on their nature, may come from different sources and are characterised by their heterogeneity. The quality of recommendations largely depends on the retrieval and representation of information. In this process there may be problems related to the diversity of types and sources of data used, or to the lack of information provided by users or recommended elements, known as a cold start [12], which can be a challenge for this type of system. The theory of semantic knowledge representation has been adopted to meet this challenge and help solve these problems [13]. In order to represent reality in a specific domain, so that it is readable by machines, the semantic web uses ontologies [14]. An ontology is a formal specification of the types, properties and interrelationships of entities for a particular domain. Since ontologies are generally built on the basis of domain expert consensus, they are highly reliable structured knowledge bases with a wide range of applications [15]. Some ontology-based RSs have been developed which allow information to be extracted and knowledge about users, items and the relationship between them to be represented in the application domain without loss of relevant information as a result of the representation. 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. As a result, based on the available information, knowledge can be exploited and updated, and new relationships between the data can be inferred. The combination of an ontology-based recommendation system with ML techniques is emerging as an increasingly promising approach to improve the accuracy of recommendations. Based on the above, and in light of the importance of developing professional skills, a hybrid recommendation system is proposed here. At its core is a semantic recommendation system that uses an ontology to model employment sectors and areas of knowledge to represent professional skills. The ontology is updated via events based on profiled data from professional records extracted from LinkedIn. ML is used to cluster entities in order to make predictions about new data. Subsequent stages make use of content filtering and heuristics to propose further lifelong learning courses for building and/or updating skills. 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.