Tourism Recommender System Based on Natural Language Classifier
Maritzol Tenemaza, José Limaico, Sergio Luján-Mora
Proceedings of the AHFE 2021 Virtual Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy (AHFE 2021), p. 230-235, July 25-29, 2021. ISBN: 978-3-030-80623-1. https://doi.org/10.1007/978-3-030-80624-8_29
(AHFE'21)
Congreso internacional / International conference
Resumen
In this paper we introduce a tourism recommender system based on tourist inquiries about a particular place in real time using an online search box. Currently, tools such as IBM Watson Natural Language Classifier based on deep learning facilitate natural language processing and classification on different categories, and consequently, the generation of recommendation systems. We present the proposed architecture and the results obtained by using this system in the historic center of Quito-Ecuador. For the evaluation, we apply a survey to test the usability and measure the usefulness of application use. This work may be useful for researchers who wish to create recommender systems based on tourist questions instantly and in real time.