Personalized Tourism Recommender System using Big Data and AI for Effective Travel Planning

With the evolution of the Internet, technology, and communication means, tourist data production has grown by many times at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.) with the evolution of Online Travel Agency (OTA). Yet, the set of options presented to tourists by these Web search engines (or even dedicated tourist websites) can be daunting and meaningful results are typically submerged in informational "noise", which hinders, or at least delays the selection process. To aid tourists in planning a trip and enable them to locate the information they seek, numerous recommender systems have been created. In this paper, we provide an overview of the different recommendation methods employed in the tourism domain. Based on this research, an architecture and a conceptual framework for a tourism recommender system are proposed, based on a hybrid recommendation method. The system proposed here is more than the suggestion of a list of tourist sites, personalized to tourist interests. It can be viewed as a trip planner that creates a detailed program, comprising heterogeneous tourism resources, for a given visit duration. The final aim is to create a recommender system based on big data technologies, artificial intelligence, and operational research to boost tourism in Morocco, particularly in the Daraˆa-Tafilalet region.