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2021 | OriginalPaper | Buchkapitel

Smart Tourism System in Calabria

verfasst von : Annarita De Maio, Daniele Ferone, Elisabetta Fersini, Enza Messina, Francesco Santoro, Antonio Violi

Erschienen in: Data Science and Social Research II

Verlag: Springer International Publishing

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Abstract

We describe a Smart Tourism System called SMARTCAL, designed in the context of a R&D project aimed at supporting the tourism development in Calabria (a region in the South of Italy). The system is designed by considering Points and Events of Interest (PEOI) and their relationship with the local transport systems and infrastructures. A proactive tourist tour planner algorithm is proposed to generate optimized itineraries based on static and dynamic profiling of the users. Social media data are also taken into account for recommendations.

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https://www.opentripplanner.org/.

Abeysundara, S., Baladasan, G., & Kodithuwakku, S. (2005). A genetic algorithm approach to solve the shortest path problem for road maps. In Proceedings of the International Conference on Information and Automation.

Ahmed, A., Kanagal, B., Pandey, S., Josifovski, V., Pueyo, L. G., & Yuan, J. (2013). Latent factor models with additive and hierarchically-smoothed user preferences. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining—WSDM ’13 (pp. 385–394). ACM Press.

Alemeye, F., & Getahun, F. (2015, September). Cloud readiness assessment framework and recommendation system. In AFRICON 2015 (pp. 1–5). IEEE.

Amin, S. A., Philips, J., & Tabrizi, N. (2019). Current trends in collaborative filtering recommendation systems. In: Y. Xia & L. J. Zhang (Eds.), Services—SERVICES 2019. Lecture Notes in Computer Science (Vol. 11517, pp. 46–60). Cham: Springer.

Ardissono, L., Goy, A., Petrone, G., Signan, M., & Torasso, P. (2003). Intrigue: Personalized recommendation of tourism attractions for desktop and handset devices. Artificial Intelligence, 17(8–9), 687–714.

Ardito, L., Cerchione, R., Del Vecchio, P., & Raguseo, E. (2019). Big data in smart tourism: Challenges, issues and opportunities.

Batet, M., Moreno, A., Sánchez, D., Isern, D., & Valls, A. (2012). Turist@: Agent-based personalised recommendation of tourist activities. Expert Systems with Applications, 39(8), 7319–7329.CrossRef

Bauer, J., & Nanopoulos, A. (2014). Recommender systems based on quantitative implicit customer feedback. Decision Support Systems, 68, 77–88.CrossRef

Bedi, P., Agarwal, S. K., Jindal, V., & Richa. (2014). MARST: Multi-Agent Recommender System for e-Tourism using reputation based collaborative filtering. In Databases in Networked Information Systems (pp. 189–201). Springer International Publishing.

Bjelica, M. (2010). Towards TV recommender system: Experiments with user modeling. IEEE Transactions on Consumer Electronics, 56(3), 1763–1769.CrossRef

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.MATH

Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41(16), 7370–7389.CrossRef

Castillo, L. A., Armengol, E., Onaindia, E., Sebastia, L., González-Boticario, J., Rodríguez, A., et al. (2008). SAMAP: An user-oriented adaptive system for planning tourist visits. Expert Systems with Applications, 34(2), 1318–1332.CrossRef

Ceccaroni, L., Codina, V., Palau, M., & Pous, M. (2009). PaTac: Urban, ubiquitous, personalized services for citizens and tourists. In Proceedings of the 3th International Conference on Digital Society (ICDS) (pp. 7–12).

Ciancio, C., De Maio, A., Laganà, D., Santoro, F., & Violi, A. A. (2018). A Genetic algorithm framework for the orienteering problem with time windows. New trends in emerging complex real life problems. AIRO Springer Series (pp. 179–188).

Colombo-Mendoza, L. O., Valencia-García, R., Rodríguez-González, A., Colomo-Palacios, R., & Alor-Hernández, G. (2017). Towards a knowledge-based probabilistic and context-aware social recommender system. Journal of Information Science, 44(4), 464–490.CrossRef

Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems—RecSys ’16 (pp. 191–198). ACM Press.

Del Vecchio, P., Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information Processing & Management, 54(5), 847–860.CrossRef

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.MathSciNetMATHCrossRef

Fernández-Tobías, I., Cantador, I., Tomeo, P., Anelli, V. W., & Di Noia, T. (2019, January). Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. In User modeling and user-adapted interaction.

Gajdošík, T. (2019). Big data analytics in smart tourism destinations. A New tool for destination management organizations? In smart tourism as a driver for culture and sustainability (pp. 15–33). Cham: Springer.

Gendreau, M., Laporte, G., & Semet, F. (1998). A tabu search heuristic for the undirected selective travelling salesman problem. European Journal of Operational Research, 106(2–3), 539–545.MATHCrossRef

Geng, X., Zhang, H., Bian, J., & Chua, T.-S. (2015, December). Learning image and user features for recommendation in social networks. In 2015 IEEE International Conference on Computer Vision (ICCV) (pp. 4274–4282). IEEE

Golden, B. L., Levy, L., & Vohra, R. (1987). The orienteering problem. Naval Research Logistics, 34(3), 307–318.MATHCrossRef

Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system. ACM Transactions on Management Information Systems, 6(4), 1–19.CrossRef

Guo, G., Qiu, H., Tan, Z., Liu, Y., Ma, J., & Wang, X. (2017). Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowledge-Based Systems, 138, 202–207.CrossRef

Hart, P. E., Nilsson, N. J., & Raphad, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernatics, 2, 100–107.CrossRef

Hassannia, R., Barenji, A. V., Li, Z., & Alipour, H. (2019). Web-based recommendation system for smart tourism: Multiagent technology. Sustainability, 11(2), 323.CrossRef

Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1), 89–115.CrossRef

Khatibi, A., Belém, F., da Silva, A. P. C., Almeida, J. M., & Gonçalves M. A. (2019). Fine-grained tourism prediction: Impact of social and environmental features. Information Processing & Management.

Kinoshita, T., Nagata, M., Shibata, N., Murata, Y., Yasumoto, K., & Ito, M. (2006) A personal navigation system for sightseeing across multiple days. In Proceedings of the 3rd International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2006) (pp. 254–259).

Konstantinos, N. A., & Zafros, K. (2009). Solving the multi-criteria time-dependent routing and scheduling problem in a multimodal fixed scheduled network. European Journal of Operation Research, 192, 18–28.MathSciNetMATHCrossRef

Kontogianni, A., Kabassi, K., Virvou, M., & Alepis, E. (2018). Smart tourism through social network user modeling: a literature review. In 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1–4). IEEE.

Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180–192.CrossRef

Koza, J. R. (1997). Genetic programming.

Kuo, T.-M., Lee, C.-P., & Lin, C.-J. (2014). Large-scale kernel rankSVM. In Proceedings of the 2014 SIAM International Conference on Data Mining (pp. 812–820). SIAM.

Liebig, T., Piatkowski, N., Bockermann, C., & Morik, K. (2017). Dynamic route planning with real-time traffic predictions. Information Systems.

Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76–80.

Liu, J., Dolan, P., & Pedersen, E. R. (2010). Personalized news recommendation based on click behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces—IUI ’10 (pp. 31–40). ACM Press.

Lorenzi, F., Loh, S., & Abel, M. (2011, August). PersonalTour: A recommender system for travel packages. In 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (pp. 333–336). IEEE.

Lozano, A., & Storchi, G. (2002). Shortest viable hyperpath in multimodal networks. Transportation Research Part B, 36, 853–874.CrossRef

Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K., & Ito, M. (2004). P–tour: A personal navigation system for tourism. In Proceedings of 11th World Congress on ITS (pp. 18–21).

Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. In Advances in neural information processing systems (pp. 1257–1264).

Moreno, A., Valls, A., Isern, D., Marin, L., & BorríS, J. (2013). SigTur/E-Destination: Ontology-based personalized recommendation of tourism and leisure activities. Engineering Applications of Artificial Intelligence, 26(1), 633–651.CrossRef

Narboneta, C. G., & Teknomo, K. (2013). OpenTripPlanner, OpenStreetMap, general transit feed specification: Tools for disaster relief and recovery. In 7th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management.

Nguyen, H., Richards, R., Chan, C.-C., & Liszka, K. J. (2016). RedTweet: Recommendation engine for reddit. Journal of Intelligent Information Systems, 47(2), 247–265.CrossRef

Nilashi, M., Dalvi-Esfahani, M., Roudbaraki, M. Z., Ramayah, T., & Ibrahim, O. (2016). A multi-criteria collaborative filtering recommender system using clustering and regression techniques. Journal of Soft Computing and Decision Support Systems, 3(5), 24–30.

Pajor, T. (2009). Multi-Modal Route Planning. Dissertation.

Pan, W. (2016). A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing, 177, 447–453.CrossRef

Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. The adaptive web (pp. 325–341). Berlin, Heidelberg: Springer.CrossRef

Pecli, A., Giovanini, B., Pacheco, C. C., Moreira, C., Ferreira, F., Tosta, F., et al. (2015). Dimensionality reduction for supervised learning in link prediction problems. In Proceedings of the 17th International Conference on Enterprise Information Systems (pp. 295–302). SCITEPRESS - Science and and Technology Publications.

Pimenidis, E., Polatidis, N., & Mouratidis, H. (2019). Mobile recommender systems: Identifying the major concepts. Journal of Information Science, 45(3), 387–397.CrossRef

Pronoza, E., Yagunova, E., & Volskaya, S. (2016). Aspect-based restaurant information extraction for the recommendation system. In Human language technology. Challenges for computer science and linguistics (pp. 371–385). Springer International Publishing.

Rodriguez, M., Posse, C., & Zhang, E. (2012). Multiple objective optimization in recommender systems. In Proceedings of the Sixth ACM Conference on Recommender Systems—RecSys ’12 (pp. 11–18). ACM Press.

Ruotsalo, T., Haav, K., Stoyanov, A., Roche, S., Fani, E., Deliai, R., et al. (2013). SmartMuseum: A mobile recommender system for the web of data. Web Semantics, 20, 50–67.CrossRef

Santiago, F. M., López, F. A., Montejo-Ráez, A., & López, A. U. (2012). GeOasis: A knowledge-based geo-referenced tourist assistant. Expert Systems with Applications, 39(14), 11737–11745.CrossRef

Sebastia, L., Garcia, I., Onaindia, E., & Guzman, C. (2008). e-Tourism: A tourist recommendation and planning application. In Proceedings of the 20th Institute of Electrical and Electronics Engineers (IEEE), International Conference on Tools with Artificial Intelligence (ICTAI) (Vol. 2, pp. 89–96).

Sierpiński, G., Celiński, I., & Staniek, M. (2014). Using trip planners in developing proper transportation behavior. International Journal of Architectural and Environmental Engineering, 8.

Souffriau, W., & Vansteenwegen, P. (2010). Tourist trip planning functionalities: State–of–the–art and future. In F. Daniel & F. M. Facca (Eds.), Current Trends in Web Engineering. ICWE 2010. Lecture Notes in Computer Science (Vol. 6385). Berlin, Heidelberg: Springer.

United Nations World Tourism Organization. (2018). UNWTO Tourism Highlights.

Verma, C., Hart, M., Bhatkar, S., Parker-Wood, A., & Dey, S. (2016). Improving scalability of personalized recommendation systems for enterprise knowledge workers. IEEE Access, 4, 204–215.CrossRef

Wang, H.-C., Jhou, H.-T., & Tsai, Y.-S. (2018). Adapting topic map and social influence to the personalized hybrid recommender system. Information Sciences.

Wang, W., Zeng, G., Zhang, D., Huang, Y., Qiu, Y., & Wang, X. (2008). An intelligent ontology and Bayesian network based semantic mashup for tourism. In Proceedings of the IEEE Congress on Services—Part I (Services) (pp. 128–135).

Yin, Z., Yueting, Z., Jiangqin, W., & Liang, Z. (2009). Applying probabilistic latent semantic analysis to multi-criteria recommender system. AI Communications, 22(2), 97–107.MathSciNetMATHCrossRef

Zhai, H., & Li, J. (2015). Refine social relations and differentiate the same friends’ influence in recommender system. In Mining intelligence and knowledge exploration (pp. 504–514). Springer International Publishing.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(5).

Ziliaskopoulos, A., & Wardell, W. (2002). An intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays. European Journal of Operational Research, 125, 486–502.MathSciNetMATHCrossRef

Titel: Smart Tourism System in Calabria
verfasst von: Annarita De Maio
Daniele Ferone
Elisabetta Fersini
Enza Messina
Francesco Santoro
Antonio Violi
Verlag: Springer International Publishing
Buch: Data Science and Social Research II
Print ISBN: 978-3-030-51221-7

Electronic ISBN: 978-3-030-51222-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-51222-4_11

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