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Currently, change in cities is advancing due to technological advances, information floods, increasing awareness, and raising living standards. Although the development of a city depends on the activation of urban marketing, an important and well-known city stands out as a brand. One of the strategies of urban management in order to progress in the competitive world market is to plan for sustainable urban branding for the city, which introducing the capabilities of the city, the most important factors in achieving economic benefits of urban branding are tourism and physical space. The metropolis of Hamadan has been selected for this purpose, including urban tourism destinations with historical and touristic species and the capital of Iran's history and civilization. The measurement tool in this study is a questionnaire that will be designed based on the conceptual framework of the research as well as related studies. For this purpose, the components and indicators of urban branding have been identified with emphasis on social, economic, cultural, and physical dimensions and have been used with the opinion of 30 urban experts and specialists and using SPSS25 software analysis at a significant level. It can be concluded that according to the respondents, these variables have a great impact on branding the metropolis of Hamadan. [ABSTRACT FROM AUTHOR]

Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user's feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposedmethod has been tried in the city of Tehran in Iran. Theresults showthat the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with 𝑘-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction. [ABSTRACT FROM AUTHOR]