## 5 User Understanding
### User's Profiling
With regards to the knowledge of users, two aspects are considered--the user profile, which is a static type of information, and its behavior in terms of \"data logs\". The static information is given by a tuple that considers ontological attributes and metadata that characterize the user profile. The User Data Log is a set of tuples associated with a set of data (e.g., timestamp) and attributes that may be useful in describing the behavior of the user or his/her activities concerning an object of interest. A project concerning a technological infrastructure for the improvement of cultural resources, named _ArkaeVision_ has been presented in [73]. The system is user-centered and it can give different manners of providing cultural heritage assets, including virtual representations of museums. The system makes it possible to adapt what is going to be presented to the user according to his/her profile. The profile is initially created through the completion of a questionnaire during registration to the platform. Subsequently, the profile is updated by keeping track of: (a) the choices made by the user during the visit, (b) the additional information that was explored, and (c) the time spent on the entire visit. In the end, the user can review his profile and he/she can participate in some games and challenges, according to the gamification paradigm. This allows the user's status to be continuously updated, adapting the users experience to their interests. A customizable system in the Cultural Heritage domain is illustrated in [74]. The system is focused on the capability of tailoring the contents to be presented according to the cognitive characteristics of a user. The system consists of a repository and four main components: the \"initialization\" component, used to initialize the context, the \"rules\" module, that is used to generate a set of rules for personalization, the \"user modeling\" element, which is responsible for generating implicitly and transparently in real time, the user model, and finally the \"personalization\" component, which provides the personalized content to the user. In particular, the user modeling is based on classifiers that correlate the cognitive characteristics of the end user with visual interaction and behavior patterns; the system captures user interactions (e.g., mouse click, key pressed, blink, location, timestamp etc.) and collects data regarding gaze and saccades calculating appropriate metrics that can be used to model the user behavior in predefined classes, called \"cognitive styles\". A hybrid recommender system in the cultural heritage domain is described in [75]. The approach is centered on User Profiling and it considers the activities of the user and his/her friends in social media as well as semantic knowledge provided by Linked Open Data (LOD). The approach extracts information from Facebook by analyzing the generated content of users and their friends, performs disambiguation by exploiting LODs, profiles the user as a social graph, and provides suggestions on cultural heritage sites or items in the vicinity of the user's current location. As for the User Modeling, a graph is generated whose vertices can be: \"user\", \"place\", \"place coordinates\", \"category of the place\"; while the edges can be \"knows\", \"visited\", \"located in\", \"has category\". In the case where social information is not available, the system nevertheless allows the user to define a profile explicitly through a questionnaire by clicking on a series of images related to 37 different categories of POIs and then updated implicitly through feedback every time the user leaves a place. In this case, the user profile is given by a vector of weights whose values express the user's interests in a given category of POIs. A methodology for improving the user experience in Cultural Heritage Institutions by capturing information from the user's social activities, rather than explicitly from user input, is illustrated in [76].