# A Design Concept for a Tourism Recommender System for Regional Development
## 5 Discussion and Conclusions  
In this paper, the authors have proposed a concept of universal information recommender system for building a personalized tourist route. The concept includes an original authorial DLT-based methodology for tourism product synthesis in order to consistently form a digital tourist avatar being a cyber-physical system. To form recommendations, it is proposed to cluster digital avatars of tourist profiles and tourism products for subsequent individualization of the tourism product offer. Despite current dynamic research on tourism recommender systems, a lot of issues and challenges have yet to be resolved [21; 49; 50; 51; 52]. Tourism recommender systems are basically focused on interaction with the target user, thus emphasizing on the design of human-machine interaction within a cyber-physical system rather than on optimizing the function of predicting results. Therefore, when developing a RS, the expediency of using new forms of interaction, such as bots or speech interfaces, is updated. Figure 5: Structure of a tourist route information recommender system. Recently, the use of neural network and deep neural network methods [53; 54; 55; 56; 57] has become a key trend. Typically, deep learning algorithms use a sequence of non-linear processing layers to represent complex features [56]. However, while recognizing their effectiveness in some areas, for example, when creating sequences of recommendations with the implicit construction of an algorithm for solving a problem, it should still be recognized that this method does not allow solving all types of recommender problems. To implement an individual tourist route, it is vital to make a decision either to visit the destination or to proceed the already started trajectory of attended POI [57; 58]. In tourism, unlike, for example, e-commerce, there is no clearly defined catalog of recommended products, since preferences are often very individual and diverse [59; 60; 61]. Thus, it is important for a RS to help a tourist with any preferences to find an interested object based on personal cultural and mental characteristics when making a decision [62]. According to some authors [63; 21; 64], first, a RS should be able to \"convince\" a tourist of the correctness of its recommendations, especially if an object (POI) is not yet known to the tourist. Second, the recommended POIs should satisfy and provide the tourist with an unforgettable experience. It should be emphasized that existing approaches often neglect a very important aspect, namely, contextual factors of the tourist route that shape travel experience: cultural characteristics of a tourist, weather conditions, travel accompaniment, travel period, etc. As for online tourism market, its major players have not yet adopted these complex decisions and are currently offering recommendations far from being personalized, as they are either based on the regular user opinion or the popularity of tourist sites [63; 64]. This case may be due to the difficulty of obtaining information about actual user behavior, that is, about actual experience of travelers and the performed sequence of actions [65; 66; 67; 68; 69; 70]. Thus, a RS in tourism lacks data on user preferences to create effective and personalized recommendations [71]. In this article, the recommender system proposed by the authors solves this problem by creating a digital tourist avatar with actual information being collected and accumulated throughout the implementation of the entire tourist route. Based on this information, the construction and subsequent training of a multilayer fuzzy neural network are implemented. This provides an opportunity both to recommend a tourist objects that he liked in the past and to indicate new potential points of the tourist route based on the clustering of digital avatars of tourist profiles and tourism products. The importance of such aspects in the construction of personalized tourist routes is noted by many authors [72; 73].