## 4 Results
Similar behaviour was observed when analysing Table 4, corresponding to the test with the total dataset, and Table 6, which showed slightly better performance in DBSCAN configuration (setting 6) over the configuration based on semantic rules (setting 3). From an analysis of Table 5 (behaviour with test data) and Table 3, a decrease in precision and an improvement in recall can be seen for the DBSCAN (setting 6) and the semantic rule-based (setting 3) configurations in relation to the previous scenarios, i.e., the size of the recommendations increased in such a way that it covered relevant recommendations but increased differences. However, looking at Table 6 (comparison of harmonic means), an improvement in the semantic rule-based configuration (setting 3) and a decrease in the DBSCAN-based configuration (setting 6), were found, although both were always above the baseline case (setting 9). This can be explained by the immunity of the ontology to the absence of data. Finally, a number of remarks can be made regarding the comparison of the capabilities and performance of the proposed system with similar works previously reviewed. The study proposed by [24] used the tasks performed by individuals to recommend jobs through a ranking process based on ratings. Our proposal makes use of the positions held by the individual and their skills, and on the basis of the skills that are most used by those who hold these positions, related positions are provided and the skills to improve or develop are predicted and recommended, together with the relevant courses. In addition, the system has the capability of recommending jobs using a filter on the percentage coverage of the user's skills from the skills of the people in that position. It is possible to establish the skills to be improved or developed and to obtain course recommendations for this purpose. In [26] an ontology was used to determine skills which calculated cosine similarity to determine similar students, and used the ratings of educational resources to make predictions. Our system does not use course ratings by individuals, since accessing this information is problematic [11]; instead, it recommends courses that improve or develop skills according to the most frequent skills found among the people who currently hold these positions. In terms of system performance against the MAE benchmark, both recommender systems behaved similarly in comparison with the DBSCAN configuration (setting 6). The study proposed by [29] determined the similarity between skills for job recommendation. However, our proposal not only checks the similarity between skills, using taxonomic similarity, but also has the ability to determine the similarity and distance between skill sets. This makes it possible to identify related jobs or is used by machine learning algorithms to cluster similar positions, and to determine the skills to improve or develop, which offers good predicting computational performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Setting** & **Test 1** & **Test 2** & **Test 3** \\ \hline
1 & 0.53 & 0.54 & 0.57 \\
2 & 0.58 & 0.61 & 0.71 \\
3 & 0.82 & 0.84 & 0.89 \\
4 & 0.81 & 0.82 & 0.86 \\
5 & 0.77 & 0.79 & 0.85 \\
6 & 0.85 & 0.85 & 0.8 \\
7 & 0.65 & 0.63 & 0.57 \\
8 & 0.8 & 0.8 & 0.77 \\
9 & 0.79 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation of the harmonic mean (F) for RS runs. Similar results were found for the measures of precision, recall and F (harmonic mean), even with alternate recommender systems between the study proposed by [30] and ours. Additionally, [30] proposed that future research could use the skills graph to recommend professional growth options for a user. Our proposal does this by using an alternative scheme, as explained above; it establishes the skills to improve or develop based on related positions, which are determined by similarity or clustering, making use of similarity with the most common skills found in the positions according to the training data set.