| Abstract |
Provide an attraction recommendation system that uses deep learning and is powered by the Internet of Things (IoT) to develop the smart city visitor experience. Users of a smart city app or website will be able to record information about their trips, including whether they are going independently or with an accompanying companion, the sort of companion (e.g., a family with children or a lover), the purpose of their trip (business or pleasure), and personal characteristics (e.g., age, interests, etc.). Using this unique combination of input data, the suggested deep learning-based recommendations system would suggest tourist activities and sights that match the user's profile nicely. In addition, the suggested tourist attraction recommendations system uses actual data obtained from Internet of Things (IoT) devices to offer content-based information (such as previously looked-at sights) and context related data (such as weather conditions, location, time of day, etc.) to potential visitors in the smart city. Regarding the initial case [(a) planning and searching for activities earlier travelling], the suggested multi-label deep learning classifiers achieve a recall, accuracy, precision, and loss of 1.6%, 98.6%, 98.8%, and 98.7%, respectively, compared to other models such as extra tree, decision tree, k-nearest neighbour, and randomized forest. As for the second scenario, it can effectively suggest tourist spots while searching for smart city activities (b) with a recall, accuracy, precision, and loss of 2.6%, 98.4%, 98.6%, and 98.7%, respectively. © 2024 IEEE. |
