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Open Access

Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework

LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez-Atlas 30000, Morocco.
IA Laboratory, Department of Computer Science, Faculty of Sciences, Moulay Ismail University, Meknes 50070, Morocco.
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With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been developed. In this article, we present an overview of the various recommendation approaches used in the field of tourism. From this study, an architecture and a conceptual framework for tourism recommender system are proposed, based on a hybrid recommendation approach. The proposed system goes beyond the recommendation of a list of tourist attractions, tailored to tourist preferences. It can be seen as a trip planner that designs a detailed program, including heterogeneous tourism resources, for a specific visit duration. The ultimate goal is to develop a recommender system based on big data technologies, artificial intelligence, and operational research to promote tourism in Morocco, specifically in the Daraâ-Tafilalet region.


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Big Data Mining and Analytics
Pages 47-55
Cite this article:
Fararni KA, Nafis F, Aghoutane B, et al. Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework. Big Data Mining and Analytics, 2021, 4(1): 47-55.








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Received: 31 July 2020
Accepted: 13 August 2020
Published: 12 January 2021
© The author(s) 2021

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