Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020015 |
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| author | Khalid AL Fararni Fouad Nafis Badraddine Aghoutane Ali Yahyaouy Jamal Riffi Abdelouahed Sabri |
| author_facet | Khalid AL Fararni Fouad Nafis Badraddine Aghoutane Ali Yahyaouy Jamal Riffi Abdelouahed Sabri |
| author_sort | Khalid AL Fararni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e08fb1738ab42f39b92ae1a80eec712 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5e08fb1738ab42f39b92ae1a80eec7122025-02-02T23:47:57ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-03-0141475510.26599/BDMA.2020.9020015Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual FrameworkKhalid AL Fararni0Fouad Nafis1Badraddine Aghoutane2Ali Yahyaouy3Jamal Riffi4Abdelouahed Sabri5<institution content-type="dept">LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz</institution>, <institution>Sidi Mohamed Ben Abdellah University</institution>, <city>Fez-Atlas</city> <postal-code>30000</postal-code>, <country>Morocco</country>.<institution content-type="dept">LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz</institution>, <institution>Sidi Mohamed Ben Abdellah University</institution>, <city>Fez-Atlas</city> <postal-code>30000</postal-code>, <country>Morocco</country>.<institution content-type="dept">IA Laboratory, Department of Computer Science, Faculty of Sciences</institution>, <institution>Moulay Ismail University</institution>, <city>Meknes</city> <postal-code>50070</postal-code>, <country>Morocco</country>.<institution content-type="dept">LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz</institution>, <institution>Sidi Mohamed Ben Abdellah University</institution>, <city>Fez-Atlas</city> <postal-code>30000</postal-code>, <country>Morocco</country>.<institution content-type="dept">LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz</institution>, <institution>Sidi Mohamed Ben Abdellah University</institution>, <city>Fez-Atlas</city> <postal-code>30000</postal-code>, <country>Morocco</country>.<institution content-type="dept">LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz</institution>, <institution>Sidi Mohamed Ben Abdellah University</institution>, <city>Fez-Atlas</city> <postal-code>30000</postal-code>, <country>Morocco</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2020.9020015recommender systemsuser profilingcontent-based filteringcollaborative filteringhybrid recommender systeme-tourismtrip planning |
| spellingShingle | Khalid AL Fararni Fouad Nafis Badraddine Aghoutane Ali Yahyaouy Jamal Riffi Abdelouahed Sabri Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework Big Data Mining and Analytics recommender systems user profiling content-based filtering collaborative filtering hybrid recommender system e-tourism trip planning |
| title | Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework |
| title_full | Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework |
| title_fullStr | Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework |
| title_full_unstemmed | Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework |
| title_short | Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework |
| title_sort | hybrid recommender system for tourism based on big data and ai a conceptual framework |
| topic | recommender systems user profiling content-based filtering collaborative filtering hybrid recommender system e-tourism trip planning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020015 |
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