## 2 Related Studies
According to [16], these systems basically need three components to provide recommendations: (1) context data (2) input data, namely, user information for the system to make a recommendation, and (3) an algorithm used in the recommendation process that operates jointly the context and input data to make recommendations for users. The basic idea of the whole system is not only the resulting recommendation algorithm, but mainly the creation of the entire process, from data acquisition through data processing to further use [23], which makes it possible to build user profiles including implicit and explicit information. In this line of research, some studies can be found that seek to make recommendations more accurate by combining different filtering techniques with the use of ontologies in order to create a model that can describe the relationship between a domain area and the users that belong to it (Table 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Objective** & **Techniques** & **Ontology** & **Assessment** & **Results** \\ \hline
[24] & 2015 & Job recommendations & Ontology Similarity algorithms & Job vacancies Tasks & Not provided & Not provided \\ \hline
[25] & 2015 & Social media & Ontology & & Quality of results and integration of functions & \\  & & recommender systems for the development of social media skills & & context & & by users & 3.62/5 \\ \hline
[26] & 2017 & Educational resources & Ontology Sequential pattern mining (SPM) & Students’ educational resources & Comparison of user suggestion between the proposed system and other RSs, without ontology, and without SPM. & Comparison with other precision by 2.8 \\ \hline
[27] & 2017 & Recommendation of massive learning activities & OntologyKnowledge-based filtering & Students’ Learning Activities & Not provided & Not provided \\ \hline
[28] & 2017 & Recommendation of learning materials & Ontology Canopy-K means Collaborative filtering & Educational Material & Comparison with other algorithm & Improved precision by 2.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RSs that use ontology. A review of the literature on the use of ontologies for RSs highlights the importance of information representation and management. The study by [25] indicated that the amount of information handled is a key factor for recommendations to be successful and proposed that future studies could do tests with real data in order to have a sufficient sample size. Likewise, [24, 26, 27, 30] pointed out that ontologies make it possible to infer information of interest to users, which can be used to enrich recommendations. Study [28] concluded that they take advantage of the similarity function to find additional information. Studies [26, 27, 28, 29] indicated that the use of the ontology improved the cold start and data sparsity problems that limit the performance of RSs. They also noted that personalising the learner profile through the use of ontology means that recommendations can be better tailored to the preferences of the target learners. In [27], the recommendations are shown in a pop-up window of the application, while the output of the system [30] is represented as a sequence graph based on the candidate skills. Study [32] concluded that knowledge representation is a vital step in building a knowledge-based recommendation for machine learning; therefore, it is essential to represent information in a machine-readable structure. Study [31] also suggested that the use of ontologies facilitates the analysis, reasoning, sharing and reuse of knowledge. In relation to combining several techniques for recommendations, [27] concluded that the main benefit is to leverage the strength of each technique to overcome the limitations of the individual techniques on their own. Study [26] proposed that further research could include other intelligent tools and technologies from fields such as data mining and machine learning in the recommendation process to further improve the performance and precision of recommendations.