A DECISION TREE BASED RECOMMENDATION SYSTEM FOR TOURISTS (International Journal of Food and Nutritional Sciences, 2022)
One of the trickiest things for travellers to do when organising a trip, both before and after, is selecting a location from the information that is accessible online and via other sources. This issue has been sought to be resolved by earlier Travel Recommendation Systems (TRSs). Nevertheless, important practical and technological factors—like system accuracy and usability and satisfaction—have been overlooked. In order to tackle this problem, a thorough comprehension of the visitors' decision-making process as well as innovative models for their information search procedure are needed. In this study, an innovative human-centric TRS that suggests travel destinations to visitors in a foreign city is proposed. It uses an actual world data set that we gathered, taking into account both technological and practical factors. To minimise the amount of inputs into the system, a two-step feature selection process was used in its development. Decision tree C4.5 is used to generate suggestions. The outcomes of the trial demonstrate that the suggested TRS is capable of offering tailored recommendations for tourism spots that travellers would find enjoyable.