Enhancing Tourism Growth Through a Hybrid Recommendation System Utilizing Web Scraping and Artificial Intelligence Techniques

Abstract

Tourism is one of the emerging areas for economic development. Tourists from all over the world are more interested in exploring the landscapes and tourist places, but they are facing so many difficulties extracting the complete information about the tourist places, hotels, flight booking and local transportation. The complexities are increased more when they are looking for a convenient schedule and budget. This research proposes a hybrid approach that uses web scraping and Artificial Intelligence (AI) cutting edge techniques to get the complete information for the tourists very easily at their convenience. The web scraping technique will extract the data from the websites of hotels, local transport, flight booking, tourist places and weather for all kinds of bookings for the tourists. This data will be given to the AI model to train the system and to get the recommendation of nearby other tourist places, hotels and transportation based on their preferences such as days and cost as a package. With this information, the tourist will enjoy the beauty of nature and explore the culture of a country. This hybrid approach will satisfy the expectation of tourist through the recommendation system and improve the growth of the tourism development of the country.