2023 International MultiConference of Engineers and Computer Scientists, IMECS 2023, Hong Kong, hkg, 5 - 07 July 2023, vol.2245, pp.82-87
As in most sectors, the development of an intelligent recommendation system in tourism becomes an important issue. Tourism agencies are putting maximum effort into suggesting the best and most valuable hotels for their customers. With the help of B2B relations between agencies and hotels, tourism agencies hold large hotel feature datasets. Summarizing or interpretation of high-quality data requires the implementation of data analysis methodologies. Tourism data is unique in terms of geography and culture. Thus, every new data set requires a dedicated analytical process. Furthermore, because raw data is in the form of a sparse binary matrix of hotel features, it poses a technical challenge to any analytical process. This paper presents a comparison of different clustering and dimension reduction methodologies for real-world hotel data of this nature. The data set represents 61% of the hotels in Turkey.