Journal Home > Volume 4 , issue 1

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.


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Khalid AL Fararni( )Fouad NafisBadraddine AghoutaneAli YahyaouyJamal RiffiAbdelouahed Sabri
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.

Abstract

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.

Keywords:

recommender systems, user profiling, content-based filtering, collaborative filtering, hybrid recommender system, e-tourism, trip planning
Received: 31 July 2020 Accepted: 13 August 2020 Published: 12 January 2021 Issue date: March 2021
References(31)
[1]
L. Sebastia, I. García, E. Onaindia, and C. Guzmán Alvarez, e-Tourism: A tourist recommendation and planning application, International Journal on Artificial Intelligence Tools, vol. 18, no. 5, pp. 717-738, 2009.
[2]
F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. Kantor, eds. Boston, MA, USA: Springer, 2011, pp. 1-35.
[3]
G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005.
[4]
M. de Gemmis, P. Lops, C. Musto, F. Narducci, and G. Semeraro, Semantics-aware content-based recommender systems, in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, eds. Boston, MA, USA: Springer, 2015, pp. 119-159.
[5]
S. Loh, F. Lorenzi, R. Saldaña, and D. Lichtnow, A tourism recommender system based on collaboration and text analysis, Information Technology & Tourism, vol. 6, no. 3, pp. 157-165, 2003.
[6]
D. Gavalas, C. Konstantopoulos, K. Mastakas, and G. Pantziou, Mobile recommender systems in tourism, Journal of Network and Computer Applications, vol. 39, pp. 319-333, 2014.
[7]
K. N. Rao and V. G. Talwar, Application domain and functional classification of recommender systems—A survey, DESIDOC Journal of Library & Information Technology, vol. 28, no. 3, pp. 17-35, 2008.
[8]
X. Y. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques, Adv. Artif. Intell., vol. 2009, p. 421425, 2009.
[9]
I. Cenamor, T. de la Rosa, S. Núñez, and D. Borrajo, Planning for tourism routes using social networks, Expert Syst. Appl., vol. 69, pp. 1-9, 2017.
[10]
G. Fenza, E. Fischetti, D. Furno, and V. Loia, A hybrid context aware system for tourist guidance based on collaborative filtering, in Proc. IEEE Int. Conf. Fuzzy Systems, Taipei, China, 2011, pp. 131-138.
[11]
K. Meehan, T. Lunney, K. Curran, and A. McCaughey, Context-aware intelligent recommendation system for tourism, presented at 2013 IEEE Int. Conf. Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, USA, 2013, pp. 328-331.
[12]
G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, Towards a better understanding of context and context-awareness, in Handheld and Ubiquitous Computing, H. W. Gellersen, ed. Berlin, Germany: Springer, 1999, pp. 304-307.
[13]
O. Boulaalam, B. Aghoutane, D. El Ouadghiri, A. Moumen, and M. L. C. Malinine, Proposal of a big data system based on the recommendation and profiling techniques for an intelligent management of moroccan tourism, Procedia Computer Science, vol. 134, pp. 346-351, 2018.
[14]
J. Borràs, A. Moreno, and A. Valls, Intelligent tourism recommender systems: A survey, Expert Systems with Applications, vol. 41, no. 16, pp. 7370-7389, 2014.
[15]
L. Ravi and S. Vairavasundaram, A collaborative location based travel recommendation system through enhanced rating prediction for the group of users, Computational Intelligence and Neuroscience, vol. 2016, p. 1291358, 2016.
[16]
K. H. Lim, J. Chan, C. Leckie, and S. Karunasekera, Personalized tour recommendation based on user interests and points of interest visit durations, in Proc. 24th Int. Joint Conf. Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 1778-1784.
[17]
I. R. Brilhante, J. A. Macedo, F. M. Nardini, R. Perego, and C. Renso, On planning sightseeing tours with TripBuilder, Information Processing & Management, vol. 51, no. 2, pp. 1-15, 2015.
[18]
K. Al Fararni, B. Aghoutane, A. Yahyaouy, J. Riffi, A. Sabri, and A. Yahyaouy, Comparative study on approaches of recommendation systems, in Proc. Embedded Systems and Artificial Intelligence, Fez, Morocco, 2019, pp.753-764.
[19]
P. Di Bitonto, M. Laterza, T. Roselli, and V. Rossano, A recommendation system to promote local cultural heritage, Journal of E-Learning and Knowledge Society, vol. 7, no. 3, pp. 97-107, 2011.
[20]
Z. Aarab, A. Elghazi, R. Saidi, and M. D. Rahmani, Toward a smart tourism recommender system: Applied to tangier city, in Innovations in Smart Cities and Applications, M. Ben Ahmed and A. Boudhir, eds. Cham, Switzerland: Springer, 2018, pp. 643-651.
[21]
J. P. Lucas, N. Luz, M. N. Moreno, R. Anacleto, A. A. Figueiredo, and C. Martins, A hybrid recommendation approach for a tourism system, Expert Systems with Applications, vol. 40, no. 9, pp. 3532-3550, 2013.
[22]
M. Hong, J. J. Jung, F. Piccialli, and A. Chianese, Social recommendation service for cultural heritage, Personal and Ubiquitous Computing, vol. 21, no. 2, pp. 191-201, 2017.
[23]
M. Vozalis and K. G. Margaritis, On the enhancement of collaborative filtering by demographic data, Web Intelligence and Agent Systems, vol. 4, no. 2, pp. 117-138, 2006.
[24]
A. T. Nguyen, N. Denos, and C. Berrut, Exploitation des données ”disponibles à froid” pour améliorer le démarrage à froid dans les systèmes de filtrage d’information, in Actes du XXIV Congrès d’INFORSID, Hammamet, Tunisie, 2006, pp. 81-95.
[25]
S. Kavitha, V. Jobi, and S. Rajeswari, Tourism recommendation using social media profiles, in Artificial Intelligence and Evolutionary Computations in Engineering Systems, S. Dash, K. Vijayakumar, B. Panigrahi, and S. Das, eds. 2017, pp. 243-253.
[26]
A. Menk, L. Sebastia, and R. Ferreira, Recommendation systems for tourism based on social networks: A survey, arXiv preprint arXiv:1903.12099, 2019.
[27]
M. Claypool, P. Le, M. Wased, and D. Brown, Implicit interest indicators, in Proc. 6th Int. Conf. Intelligent User Interfaces, Santa Fe, NM, USA, 2001, pp. 33-40.
[28]
R. Burke, Hybrid web recommender systems, in the Adaptive Web. Lecture Notes in Computer Science, vol 4321, P. Brusilovsky, A. Kobsa, and W. Nejdl, eds. Berlin, Germany: Springer, 2007, pp. 377-408.
[29]
K. Hafed, Y. Fakhri, S. Boulaknadel, A. Moumen, H. Jamil, and B. Aghoutane, Decisional information systems of the public actors in Moroccan Oasis Zones: Case study Draa-Tafilalet region: Towards a descriptive approach and a measurement of qualities and performances, presented at 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2018.
[30]
F. Nafis, A. Yahyaouy, and B. Aghoutane, Towards a generic ontology for describing heritage data, in Conférence Afro-Méditerranéenne Sur La Recherche Multidisciplinaire & Applications, Rabat, Maroc, 2018.
[31]
F. Nafis, A. Yahyaouy, and B. Aghoutane, Ontologies for the classification of cultural heritage data, in Proc. 5th Int. Conf. Wireless Technologies, Embedded and Intelligent Systems, Fez, Morocco, 2019, pp. 1-7.
Publication history
Copyright
Rights and permissions

Publication history

Received: 31 July 2020
Accepted: 13 August 2020
Published: 12 January 2021
Issue date: March 2021

Copyright

© The author(s) 2021

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Reprints and Permission requests may be sought directly from editorial office.

Return