Una revisión de los sistemas recomendadores grupales como herramienta innovadora en el área del turismo
Barra lateral del artículo
Contenido principal del artículo
Resumen
Los sistemas recomendadores son herramientas enfocadas en ayudar a los usuarios a obtener la información que mejor se corresponde con sus intereses y preferencias en un espacio de búsqueda sobrecargado de posibles opciones. A su vez, los sistemas de recomendación grupales se centrar en la sugerencia de determinados tipos de ítems que tienden a ser consumidos en grupos. El presente trabajo se centra en realizar un análisis de la utilización de los sistemas de recomendación grupal como herramienta innovadora en el área del turismo, la cual constituye una de las áreas de aplicación más importantes de los sistemas de recomendación. Específicamente, se lleva a cabo un estudio de los trabajos más recientes en esta línea de investigación, realizándose una comparación atendiendo a tipo de estudio, tipo de evaluación, escenario de aplicación, fortalezas, debilidades, y país en que se desarrolla el estudio. Esta comparación dio lugar a la proyección de líneas futuras entre las que se destaca el desarrollo de investigaciones de ciclo completo que incluyan el desarrollo de nuevos algoritmos y metodologías que concluyan con un extenso estudio experimental que garantice una apropiada reutilización de los métodos propuestos.
Descargas
Detalles del artículo
La Revista de Ciencia y Tecnología sostiene su compromiso con las políticas de Acceso Abierto a la información científica, al considerar que tanto las publicaciones científicas como las investigaciones financiadas con fondos públicos deben circular en Internet en forma libre y gratuita. Los trabajos publicados en la Revista de Ciencia y Tecnología están bajo la licencia Creative Commons Atribución-NoComercial 2.5 Argentina.
Aceptado 2021-04-16
Publicado 2021-08-06
Citas
A. Bellogín, et al., "An empirical comparison of social, collaborative filtering, and hybrid recommenders," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, p. 14, 2013.
A. Delic, et al., "An observational user study for group recommender systems in the tourism domain," Information Technology & Tourism, vol. 19, pp. 87-116, 2018.
A. Gunawardana and G. Shani, "A Survey of Accuracy Evaluation Metrics of Recommendation Tasks," Journal of Machine Learning Research, vol. 10, pp. 2935-2962, 2009.
A. Javari and M. Jalili, "A probabilistic model to resolve diversity–accuracy challenge of recommendation systems," Knowledge and Information Systems, vol. 44, pp. 609-627, 2015.
C. Porcel, et al., "A hybrid recommender system for the selective dissemination of research resources in a technology transfer office," Information Sciences, vol. 184, pp. 1-19, 2012.
D. Bernardes, et al., "A Social Formalism and Survey for Recommender Systems," ACM SIGKDD Explorations Newsletter, vol. 16, pp. 20-37, 2015.
D. Herzog, et al., "TourRec — A Tourist Trip Recommender System for Individuals and Groups," in Proceedings of the 12th ACM Conference on Recommender Systems, 2018, pp. 496-497.
D.-Y. Yeh and C.-H. Cheng, "Recommendation system for popular tourist attractions in Taiwan using Delphi panel and repertory grid techniques," Tourism Management, vol. 46, pp. 164-176, 2015.
E. Carballo-Cruz, et al., "An Intelligent System for Sequencing Product Innovation Activities in Hotels," IEEE Latin America Transactions, vol. 17, pp. 305-315, 2019.
E. Carballo-Cruz, et al., "Del desarrollo de capacidades de aprendizaje a la satisfacción del cliente en una instalación hotelera," Investigaciones Turísticas, vol. 2, pp. 82-101, 2011.
E. Carballo-Cruz, et al., "La innovación de producto en la formación de la imagen percibida. Caso Hotel Colonial Cayo Coco, destino turístico Jardines del Rey, Cuba," Retos de la Dirección, vol. 10, pp. 114-141, 2016.
F. Ricci, et al., Recommender Systems Handbook 2nd Edition. New York: Springer, 2015.
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, pp. 734-749, 2005.
G. Pavlidis, "Recommender systems, cultural heritage applications, and the way forward," Journal of Cultural Heritage, vol. 35, pp. 183-196, 2019.
G. Shani and A. Gunawardana, "Tutorial on application-oriented evaluation of recommendation systems," AI Communications, vol. 26, pp. 225-236, 2013.
I. Christensen, et al., "Social group recommendation in the tourism domain," Journal of Intelligent Information Systems, vol. 47, pp. 209-231, 2016.
J. Álvarez and J. Ziegler, "Negotiation and Reconciliation of Preferences in a Group Recommender System," Journal of Information Processing, vol. 26, pp. 186-200, 2018.
J. Bobadilla, et al., "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.
J. Castro, et al., "An empirical study of natural noise management in group recommendation systems," Decision Support Systems, vol. 94, pp. 1-11, 2017.
J. Lu, et al., "Recommender system application developments: A survey," Decision Support Systems, vol. 74, pp. 12-32, 2015.
J. N. Cappella, et al., "Constructing Recommendation Systems for Effective Health Messages Using Content, Collaborative, and Hybrid Algorithms," The ANNALS of the American Academy of Political and Social Science, vol. 659, pp. 290-306, 2015.
K. Chaudhari and A. Thakkar, "A Comprehensive Survey on Travel Recommender Systems," Archives of Computational Methods in Engineering, vol. 27, pp. 1545-1571, 2020.
K. Choi, et al., "A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis," Electronic Commerce Research and Applications, vol. 11, pp. 309-317, 2012.
K. Seaborn and D. I. Fels, "Gamification in theory and action: A survey," International Journal of human-computer studies, vol. 74, pp. 14-31, 2015.
L. Lü, et al., "Recommender systems," Physics Reports, vol. 519, pp. 1-49, 2012.
L. Sun, et al., "Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews," World Wide Web, vol. 22, pp. 83-100, 2019.
M. G. Vozalis and K. G. Margaritis, "Using SVD and demographic data for the enhancement of generalized collaborative filtering," Information Sciences, vol. 177, pp. 3017-3037, 2007.
M. J. Pazzani, "A framework for collaborative, content-based, and demographic filtering.," Artificial Intelligence Review, vol. 13, pp. 393-408, 1999.
M. Kompan and M. Bielikova, "Group recommendations: Survey and perspectives," Computing and Informatics, vol. 33, pp. 1-31, 2014.
M. Li, et al., "An approach to task-oriented knowledge recommendation based on multi-granularity fuzzy linguistic method," Kybernetes, vol. 44, pp. 460-474, 2015.
P. Alves, et al., "Modeling a Mobile Group Recommender System for Tourism with Intelligent Agents and Gamification," in International Conference on Hybrid Artificial Intelligence Systems, 2019, pp. 577-588.
P. Favardin, et al., "Borda rule, Copeland method and strategic manipulation," Review of Economic Design, vol. 7, pp. 213-228, 2002.
P. Lops, et al., "Content-based recommender systems: state of the art and trends " in Recommender Systems Handbook, F. Ricci, et al., Eds., ed New York: Springer, 2011, pp. 73-100.
R. Burke, "Hybrid recommender systems:survey and experiments," User Modeling and User-Adapted Interaction, vol. 12, pp. 331-370, 2002.
R. Hti and M. S. Desarkar, "Personalized tourist package recommendation using graph based approach," in Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, 2018, pp. 257-262.
R. Logesh, et al., "Efficient user profiling based intelligent travel recommender system for individual and group of users," Mobile Networks and Applications, vol. 24, pp. 1018-1033, 2019.
R. Yera and L. Martínez, "A recommendation approach for programming online judges supported by data preprocessing techniques," Applied Intelligence, vol. 47, pp. 277-290, 2017.
R. Yera and L. Martínez, "Fuzzy tools in recommender systems: A survey," International Journal of Computational Intelligence Systems, vol. 10, pp. 776-803, 2017.
R. Yera Toledo, et al., "A recommender system for programming online judges using fuzzy information modeling," in Informatics, 2018, p. 17.
R. Yera, et al., "A Food Recommender System Considering Nutritional Information and User Preferences," IEEE Access, vol. 7, pp. 96695-96711, 2019.
R. Yera, et al., "A Regularity-Based Preprocessing Method for Collaborative Recommender Systems," Journal of Information Processing Systems, vol. 9, pp. 435-460, 2013.
S. Dara, et al., "A survey on group recommender systems," Journal of Intelligent Information Systems, vol. 54, pp. 271-295, 2020.
S. Renjith, et al., "An extensive study on the evolution of context-aware personalized travel recommender systems," Information Processing & Management, vol. 57, p. 102078, 2020.
T. De Pessemier, et al., "Comparison of group recommendation algorithms," Multimedia tools and applications, vol. 72, pp. 2497-2541, 2014.
T. De Pessemier, et al., "Hybrid group recommendations for a travel service," Multimedia tools and applications, vol. 76, pp. 2787-2811, 2017.
T. N. Nguyen and F. Ricci, "A chat-based group recommender system for tourism," Information Technology & Tourism, vol. 18, pp. 5-28, 2018.
W. Wang, et al., "Member contribution-based group recommender system," Decision Support Systems, vol. 87, pp. 80-93, 2016.
X. Su, et al., "Hybrid collaborative filtering algorithms using a mixture of experts," in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI ’07), Silicon Valley, Calif, USA, 2007, pp. 645-649.
Z. Huang, et al., "A comparison of collaborative-filtering recommendation algorithms for e-commerce," IEEE Intelligent Systems, pp. 68-78, 2007.