## 5 User Understanding
### User's Profiling
The CURE (Cultural User peRsonas Experience) methodology is based on three phases: _\"data acquisition\"_, _\"reasoning\"_, and _\"dissemination\"_. The \"data acquisition\" collects data from various sources (mobile devices, social media, open access web data) and applies data mining and natural language processing techniques to obtain the user profile. Moreover, the \"data acquisition\" phase integrates data from different sensors and context-aware algorithms. During the reasoning process, the data are analyzed to create a user model, while the dissemination phase deals with spreading the profile in the application to provide personalized content for the user. A methodology for adaptive and context-aware system design has been presented in [77]. The approach exploits a formal semantic model of intelligent environments and a behavioral approach based on rules. The system has been implemented in a real museum and collects different environmental parameters of the rooms like humidity, temperature, and position sensors. Furthermore, in our user understanding view, the approach is relevant also because it acquires knowledge about the user preferences explicitly for different characteristics like his preferred historical period, the preferred style, and other kinds of information like the user's predisposition to walk. Moreover, an implicit knowledge about the user is inferred by analyzing his browsing history and social network contents that have been published or reported. The environment is modeled by using a context ontology, while the visit path is characterized by a set of metadata and a set of context information. The system uses an importance ranking method as a recommendation strategy. The relevance of understanding users is also highlighted in [78], where a complete and comprehensive survey on the so called _\"personalized access to cultural heritage\"_ is provided. It gives an overview of the different user models (overlay, feature-based, content-based, list of items) that can also be dynamically adapted with various techniques, like heuristic inference, activation/inhibition networks, collaborative filtering, content-based filtering, and semantic reasoning. Moreover, in that paper, different techniques are also highlighted to match the user preferences with the specific kind of content to provide at the right time. Among these techniques, the survey reports condition-action rules, ranking, semantic reasoning, activation/inhibition networks, collaborative filtering, content-based filtering, social recommendation methodologies, models based on vector spaces, and hybrid approaches. Finally, a set of challenges for user modeling and personalization are outlined in [79]; in particular, the scrutable user modeling and adaptation, which becomes very hard when the adaptation is based on collective behavior: data mining, machine learning techniques, and deep neural networks are not still capable of explaining the conclusions that lead to a specific recommendation. A second issue is the repeatability of the research results in different contexts. The third challenge is the meta-adaption capable of avoiding filter bubbles, that is, when the user cannot easily access items that are not recommended.