期刊
NEURAL COMPUTING & APPLICATIONS
卷 34, 期 10, 页码 7691-7709出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-06872-0
关键词
Deep learning; Deep neural networks; Multi-label classification; IoT architecture
资金
- Agencia Estatal de Investigacion of Spain [PID2019-108713RB-C51/AEI/10.13039/501100011033]
We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. The system takes into account personal input features and real-time context information to recommend suitable tourist activities and attractions, resulting in an improved tourist experience.
We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.
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