## 2 Background Knowledge
### User Personalisation  
Personalisation has become more critical in computer science, particularly in recommendation systems. A recommender system may interact with many users, each of whom has their unique set of preferences. The recommender system is responsible for addressing the needs of each user by recommending user-specific content or adjusting itself to meet those needs. As a result, the user profile improves the recommender system in understanding user requirements and acting accordingly. The technique of identifying data about a user's activity is known as user profiling. The system may utilise this information to learn more about the user, and this knowledge can then be applied to improving retrieval and ensuring the user's satisfaction. User profiling involves two crucial aspects: effectively understanding the user and proposing content of his interest based on that knowledge [22]. When a visitor uses a system for the first time, the system will most likely fail to recommend content to the user effectively. This problem is commonly known as cold start and is common in recommendation systems. Many solutions and methods have been proposed to address the cold start issue. Standard recommendation strategies are based on association rules and clustering techniques [23], social information [24], ontological classification of knowledge [25] and hybrid user modelling [26]. Multiple methods have been proposed to address the user profiling and classification task, particularly in the CH domain, improving the overall UX. A relatively common technique is classifying users under persona profiles based on replies to multiple-choice queries. [PERSON] et al. [27] employed heuristic techniques to represent the requirements of distinct groups of visitors, whereas [18] used personas to convey the needs of different groups of visitors. Others asked users direct questions to establish their profiles, although research has shown that visitors dislike direct questioning [28]. Because direct asking was viewed as invasive, [29] took a detour and invited visitors to create avatars mirroring their personality attributes. Because visitors' time is limited and important, some tactics were more subtle and did not necessitate any visitor behaviours. One research, for example, simply monitored how visitors walked throughout museums and matched their movement patterns to personality traits [30]. As a result, it appears that such feedback acquisition methodologies used to deal with the cold start problem differ and may be classified as explicit (needing visitor activities) or implicit (not requiring visitor actions). Moreover, explicit ways might be direct (asking the visitor questions) or indirect (using the visitor in an activity that does not appear to be directly connected to profiling, such as games) [31]. While user profiling is beneficial in cultural heritage, establishing accurate visitor profiles is difficult due to the short time of most trips and the fact that most people would only visit a particular place once. Within these time constraints, visitor profiles must be developed fast and efficiently in terms of their suitability for various visitors. The difficulty is that, while visitors appreciate the benefits of customisation in cultural heritage, they are averse to form-filling activities. Researchers must become more innovative in collecting the necessary data to build user profiles. Indeed, creating user profiles as \"fictional\" characters based on real data and research and tailored to a specific audience is regarded as a very consistent and representative method of defining genuine users and their objectives. However, it is critical to determine the precise (minimum) number of personas for each occasion to concentrate on the visitor characteristics to be investigated. As a design tool, personas effectively communicate behaviours, objectives, desires, requirements, and frustrations.