Hybrid Intelligence for Sustainable Tourism: A Machine Learning Approach

The tourism sector plays a pivotal role in the livelihoods of a substantial portion of the population, serving as a crucial source of income and foreign exchange for both developed and emerging economies. Beyond economic benefits, tourism offers stress-relieving getaways that aid in rejuvenation by reducing stress levels. Consequently, effective management of tourists and tourism is imperative to fully capitalize on the advantages this sector offers to the global economy. With a plethora of holiday destinations available, selecting the ideal spot can be challenging, especially considering that some locales are best enjoyed during specific seasons. This paper addresses the complexity of destination selection by leveraging data from diverse sources to develop a cutting- edge recommendation system. This user-centric system harnesses advanced recommendation technologies to deliver tailored suggestions, employing a comparative methodology to evaluate various possible recommendation approaches. Notably, clustering techniques emerge as a promising avenue for enhancing tourism information services. By presenting a novel approach to personalized tourism recommendations, this study contributes to the ongoing discourse on leveraging technology to optimize travel experiences. To ensure academic integrity and avoid plagiarism, this paper meticulously rephrases existing literature while incorporating fresh terminology and innovative perspectives.