## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & [25] & [27] & [31] & [11] & **Author’s Proposal** \\ \hline Type & Hybrid RS & Hybrid RS & Hybrid RS & Hybrid RS & Hybrid RS \\ \hline Items to be & & MOOC & MOOC & University courses & Lifelong learning & Lifelong learning \\ recommended & & & & & \\ \hline Use of social & & & & & \\ network & Yes & - & No & Yes & Yes \\ \hline Semantic & Yes & Yes & Yes & No & Yes \\ \hline Ontology & Yes & Yes & Yes & No & Yes \\ \hline Machine learning & No & No & Yes & No & Yes \\ \hline Types of data & - & - & Structured & \begin{tabular}{c} Semi-structured/ \\ Unstructured \\ \end{tabular} & \begin{tabular}{c} Semi-structured/ \\ Unstructured \\ \end{tabular} \\ \hline Future scenarios & No & No & Yes & Yes & Yes \\ \hline Harmonic mean & - & - & - & 0.79 & \
\begin{tabular}{c} 0.85 \\ ML/DB-SCAN \\ \end{tabular} \\ \hline Novelty/Serendipity & - & - & - & 0.52/0.06 &
\begin{tabular}{c} 0.52/0.07 \\ ML/DB-SCAN \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of research results. The use of semantic rules improves recommendations by improving refining of the filtering of positions (according to the hierarchy) and therefore of skills, as well as considering related skills. This is an improvement over [11], which suggested skills development courses for lower level positions due to it not consider job ranking or the option of user promotion. With respect to the weaknesses found in our proposal, we highlight the fact that the ontology is not automatically updated. Future work may use ontologies from third parts that are updated frequently for job sectors and areas of knowledge. Another aspect to improve is the user profile, incorporating learning style and incorporating the learner's preferences in terms of language or modality. For this purpose, future works could include information from other social networks to complement user profiles. The use of LinkedIn skills endorsements to improve recommendations could be considered. Further research could involve working with several related domains (software development, technical support, consultancy, training and academia), in order to evaluate the behaviour of the system for multiple domains, and updating the ontology with new instances associated to new domains. The differences in performance when using several clustering algorithms suggests considering other algorithms and evaluating the use of soft versus hard clustering, as well as reviewing the weighted or conditional use of multiple techniques and algorithms in the recommendation construction strategy, according to the nature of the user. Reviewing the weighted or conditional use of multiple techniques and algorithms in the recommendation construction strategy could be evaluated according to the nature of the user. The use of algorithmic techniques on the knowledge graph can be evaluated to improve recommendations based on the most in-demand positions and skills, and build pathways describing sequences to achieve skills. In conclusion, we can highlight the following:  
Based on the weaknesses and opportunities resulting from the evaluation of the recommender system based on stored data [11], it was proposed a hybrid recommender system based on semantics be developed, for which an ontology was built and updated using machine learning. The results obtained showed that the modeling represented reality quite well, that the use of an ontology (instead of a taxonomy and stored data) allowed good modeling of reality which not only resulted in a better quality of recommendations, but also in a simpler system with fewer steps, and which translated into a better performance in relation to the previous system based on stored data [11]. In the absence of training data, the use of ontology could generate acceptable recommendations; an expected result in the use of semantic recommenders.