# Context-Based Model for Browsing the Web Through Voice
## 5 Model Functionalities for Voice-Based Web Browsing
### User Context Generation
#### 5.1.1 User Profile  
Currently, there is no consensus on how to model the context of an entity, as context models are often specific to the problem domain being addressed. This is because the context varies significantly depending on the application domain, such as health, education, or transportation. In this model, the data that make up the user profile represent their characteristics and their relationship with the environment, specifically in the domain of web browsing. Since it is possible to include a large amount of data in this domain, those that we consider most significant were selected. Therefore, the user profile is structured from the data included in the following four categories:  
Figure 3: Context-based model functionalities for browsing the Web using voice. Demographic data: they provide important information about the characteristics of the population to which the user belongs. The demographics used in this model are:  
Age: it allows understanding the different stages of life the user is going through and how these may influence their needs, behaviors, and preferences;  
Gender: it facilitates understanding of the user's perception of the world, their roles, and consumption patterns;  
Address: it provides information about the environment where the user lives, such as geographic and cultural characteristics that may influence their behavior and specific needs;  
Educational level: it provides insight into the user's level of knowledge, skills, and access to opportunities, which may have an impact on their behavior, decision-making, and perspectives. 2. Topics of interest: on the Web, the user can find a wide variety of content on various topics such as politics, science, technology, art, and history. Knowing the topics of interest to the user can improve the browsing experience by making it easier to find and access meaningful information. 3. Personal affinities: it allows knowing the user's interests, likings, or preferences. In this category, the following data are considered:  
Activities and hobbies: it allows knowing the activities that the user enjoys doing in their free time, such as sports, music, reading, art, movies, travel, or cooking;  
Favorite music and movies: it allows knowing the musical and cinematographic genres preferred by the user, as well as the movies and music that they consult the most. It also provides the opportunity to know the artists, bands, or directors that the user appreciates;  
Books and readings: it allows knowing the literary genres that the user prefers, if they have favorite authors or books, or if they like to read novels, essays, biographies, or other genres;  
Sports and physical activities: it allows knowing if the user is interested in any particular sport, if they like to exercise, participate in outdoor activities or follow any sports team or competition;  
Food and gastronomy: it allows knowing the user's culinary preferences, if they enjoy any particular type of cuisine or have favorite restaurants or dishes. 4. Website preferences: it allows the user's preferences to be known when choosing to access certain websites to search for information. This data are collected through a voice survey conducted during the application setup phase. Figure 4 shows the sequence diagram corresponding to the collection of user profile data. This diagram illustrates the interaction between the components of the model to collect and process information through a voice survey, as described below.