## 2 Background  
Information dashboards and visualizations have increased their popularity throughout the years. They enable people to understand complex datasets and gain insights into different domains. However, these tools are not suitable for any context and need to match specific requirements in different situations. Given the necessity and potential benefits of tailored dashboards, several solutions, and proposals to address dashboards' tailoring capabilities can be found in the literature [12].  
Selecting the right visual metaphor or encoding is challenging when dealing with dashboards or visualizations. However, this task is essential due to the influence of these design decisions in the effectiveness of the dashboard or visualizations, because a wrong visual metaphor or encoding could lead to mistakes when interpreting data.  
Several tools have been proposed to tackle this issue by automatically recommending visualizations. These works point out different factors that influence the specific design of an information visualization, such as the tasks that will be carried out through the visualization, the users' characteristics, the users' behavior, or the dataset structure and characteristics [26].  
How can these aspects support an automatic process for recommending the best encodings or visual metaphors to foster knowledge discovery? There are different approaches that try to ease the process of generating visualizations. For example, some methods use visual mapping and rules to recommend a certain visualization based on the target data to be displayed [26]. An algorithm could infer through hardcoded guidelines and rules which visual mark, scale, encoding, etc., suits better the target context by using information regarding the dataset characteristics, such as its structure or data types. Tableau's Show Me [27], Manyeyes [28], or Voyager [29], are some of the tools that use visual mapping to offer tailored recommendations for information visualizations.  
The context of application is also a relevant factor when designing visualizations. For example, the work presented in [30] employs a visualization ontology named VISO to annotate data and execute a ranking process. The outcomes of the ranking process are ratings that measure the suitability of visual encodings. End-users' preferences and characteristics are also taken into account in some visualization recommendation strategies. VizDeck [31] analyzes the input data and proposes a set of visualization that the user must rank, selecting the ones that fit better her requirements. This process supports subsequent recommendations because the system learns from the users' interactions.  
On the other hand, content and collaborative filtering can also be employed to recommend visualizations [32]. These recommendation strategies yield potentially suitable visualizations for specific users.  
Applications of neural networks to infer specific features of a visualization can also be found. For example, VizML [33] used data from Plotly ([[https://plot.ly/](https://plot.ly/)]([https://plot.ly/](https://plot.ly/))) to analyze graphics that were developed to visualize different datasets. Using this Plotly's graphics corpus, they obtained a model that automatically infers the best characteristics that a visualization should have based on the input data. A similar approach is taken in Data2 Vis [34], where data characteristics are \"translated\" into a concrete visualization specification.  
However, these approaches are mainly focused on users' or data characteristics. Some works propose to take into account visualization tasks to rank the effectiveness of two-dimensional visualization types in order to choose the best for each task [35]. As introduced before, the users' goals are crucial to craft effective visualizations, so the potentially involved tasks to reach them must be effectively supported by the generated visualization [36].