# A Design Concept for a Tourism Recommender System for Regional Development
## 4 Results  
### Methodology for Tourism Product Formation Based on User Preferences  
The methodology for the formation and recommendation of tourism products to end users is presented in the form of a number of major stages, which, in turn, are a sequence of algorithmic steps implemented in the form of client and server components of a digital tourism recommender ecosystem. The first two stages are the collection of data on the key points of possible tourism products and the collection of data on the tourism preferences of users, including their personal data. Both steps are performed in parallel by different applications (Figure 2). At the same time, the information received earlier should be periodically updated to update the data, both about key points and about the user and his changing capabilities and preferences. (A) The first stage is the collection, cleaning, and consolidation of big data on the possible components of future tourism products. The components of a detailed tourism product are:  
1. Spatial and temporal parameters of the start and end points of routes. Retrospective and predicted weather data specific to the spatial and temporal parameters of the routes. 2. Sets of key points of the future route, including the coordinates of tourist attractions, the coordinates of places of possible stops (parking lots), the coordinates of public catering places, the coordinates of places for boarding public transport (airports, railway stations and bus stations, places for ordering transfers, car rentals, carsharing, etc. ), coordinates of places of accommodation for tourists, tourist and information centers, etc. 3. Spatial parameters and time lags of movements between key points. Spatial and temporal data are needed to determine the logistics of travel, select the type of transport, purchase tickets, etc. since attractions can be located at remote distances. 4. Cost parameters of movement (cost of tickets, fuel, transport rental, transfer), cost parameters of visits to key points (cost of entry, excursion service, parking, etc.). 5. Sufficiently detailed information about the key points of the tourist product (attractions, places of stops, overnight stays, meals, gas stations, etc.). The data are collected by various mapping and navigation services (Google Map, Maps.Me, Yandex Map, etc. ), as well as booking services (Booking, AirBrib, Ostrovok, Kvartirka, Sutochno.ru, etc. ), which are in the public domain access. Therefore, an important task is the integration of the recommender ecosystem with third-party services to obtain the required information by users, as well as the availability of links to various sites associated with key points on the Internet. Since existing services do not provide the ability to download the collected data, the integration is the addition of many links to third-party resources tied to the coordinates of key points. 6. Links to reviews of travelers who have previously visited key points. Real reviews of key waypoints are important when synthesizing or selecting a tourism product according to user preferences. It is very important to have up-to-date feedback from other people (within a few months), including negative information. 1. Information about the key points of the tourist route and other parameters of the tourist route, as a rule, is presented in a variety of sources on the Internet, therefore this stage is implemented using the crawling technology with the cleaning of the received data from duplicates and information noise. The search and collection of information about key points is carried out by crawling various sources and consolidating links to resources with national information. At the same time, bypassing previously found links should be periodically repeated to update information and remove obsolete and empty links. 2. To reduce information noise at the consolidation stage, the found links to the same resources and duplicates of information on different sites and services are checked and excluded.