Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information “noise.” This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations.