Skip to main content
SpringerLink
Log in

Exploiting social data for tourism management: the SMARTCAL project

  • Published:
Quality & Quantity Aims and scope Submit manuscript

Abstract

In this work we describe a new Smart Tourism System called SMARTCAL, born during the development of a R&D project for supporting the tourism digitalisation, that includes the release of a pilot in Calabria (a region in the South of Italy). The project is a new initiative to support tourism and hospitality industry with a series of statistical tools for the decision makers, to provide digital and smart services for the tourists that want to build their itineraries with flexibility and to improve the valorisation of a particular territory from the economical and tourist point of view. Indeed, the system is designed by considering Points and Events of Interest (PEOI) and their relationship with the local transport systems, with the hospitality industries and with the policy makers. Two major tools are described in the following: a proactive tourist tour planner algorithm, proposed to generate optimised itineraries based on static and dynamic profiling of the users, and a sentiment analysis module that supports decision makers with a scorecard with a set of key indicators.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

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

  • Alter, S.L.: Decision Support Systems: Current Practice and Continuing Challenges. Addison-Wesley, Reading (1980)

    Google Scholar 

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

    Google Scholar 

  • Ardito, L., Cerchione, R., Del Vecchio, P., Raguseo, E.: Big data in smart tourism: challenges, issues and opportunities. Curr. Issues Tour. 22, 1805–1917 (2019)

    Article  Google Scholar 

  • Baggio, R., Caporarello, L.: Decision support systems in a tourism destination: literature survey and model building. In: itAIS 2005—2nd Conference of the Italian Chapter of AIS (Association for Information Systems) (2005)

  • Baldi, M., Fersini, E., Messina, E.: Relational Bayesian model averaging for sentiment analysis in social networks. In: Sixth International Conference on Machine Learning, Optimization, and Data Science (2020)

  • Batet, M., Moreno, A., Sánchez, D., Isern, D., Valls, A.: Turist@: agent-based personalised recommendation of tourist activities. Expert Syst. Appl. 39(8), 7319–7329 (2012)

    Article  Google Scholar 

  • Bauer, J., Nanopoulos, A.: Recommender systems based on quantitative implicit customer feedback. Decis. Support Syst. 68, 77–88 (2014)

    Article  Google Scholar 

  • Bedi, P., Agarwal, S.K., Jindal, V., Richa: MARST: multi-agent recommender system for e-tourism using reputation based collaborative filtering. In: Databases in Networked Information Systems. Springer, pp. 189–201 (2014)

  • Bjelica, M.: Towards TV recommender system: experiments with user modeling. IEEE Trans. Consum. Electron. 56(3), 1763–1769 (2010)

    Article  Google Scholar 

  • Borràs, J., Moreno, A., Valls, A.: Intelligent tourism recommender systems: a survey. Expert Syst. Appl. 41(16), 7370–7389 (2014)

    Article  Google Scholar 

  • Castillo, L.A., Armengol, E., Onaindia, E., Sebastia, L., González-Boticario, J., Rodríguez, A., Fernández, S., Arias, J.D., Borrajo, D.: SAMAP: an user-oriented adaptive system for planning tourist visits. Expert Syst. Appl. 34(2), 1318–1332 (2008)

    Article  Google Scholar 

  • Ciancio, C., De Maio, A., Laganà, D., Santoro, F., Violi, A.: A genetic algorithm framework for the orienteering problem with time windows. In: New Trends in Emerging Complex Real Life Problems AIRO. Springer, pp. 179–188 (2018)

  • Dennison, G., Bourdage-Braun, M., Chetuparambil, S.: Social commerce defined. White Paper 23747 (2009)

  • Eom, S.B., Lee, S.M., Kim, E.B., Somarajan, C.: A survey of decision support system applications (1988–1994). J. Oper. Res. Soc. 49, 109–120 (1998)

    Article  Google Scholar 

  • Fersini, E., Messina, E., Pozzi, F.: Sentiment analysis: Bayesian ensemble learning. Decis. Support Syst. 68, 26–38 (2014)

    Article  Google Scholar 

  • Fersini, E., Pozzi, F., Messina, E.: Approval network: a novel approach for sentiment analysis in social networks. World Wide Web 20, 831–854 (2017)

    Article  Google Scholar 

  • Finlay, P.N.: Introducing Decision Support Systems. Blackwell, Oxford (1994)

    Google Scholar 

  • Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: A survey on algorithmic approaches for solving tourist trip design problems. J. Heuristics 20, 291–328 (2014)

    Article  Google Scholar 

  • Gendreau, M., Laporte, G., Semet, F.: A tabu search heuristic for the undirected selective travelling salesman problem. Eur. J. Oper. Res. 106(2), 539–545 (1998)

    Article  Google Scholar 

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

  • Golden, B., Levy, L., Vohra, R.: The orienteering problem. Naval Res. Logist. 34, 307–318 (1987)

    Article  Google Scholar 

  • Gunawan, A., Chuin Lau, H., Vansteenwegen, P.: Orienteering problem: a survey of recent variants, solution approaches and applications. Eur. J. Oper. Res. 255(2), 315–332 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)

    Article  Google Scholar 

  • Jriji, D., Krichen, S., Madany, F.: A memetic algorithm for the tourist trip design with clustered points of interests. In: 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), pp. 1–6 (2020)

  • Kabassi, K.: Personalizing recommendations for tourists. Telemat. Inform. 27(1), 51–66 (2010)

    Article  Google Scholar 

  • Kinoshita, T., Nagata, M., Shibata, N., Murata, Y., Yasumoto, K., Ito, M.: 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 (2006)

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

  • Kotkov, D., Wang, S., Veijalainen, J.: A survey of serendipity in recommender systems. Knowl. Based Syst. 111, 180–192 (2016)

    Article  Google Scholar 

  • Kinnear, K. E.: Advances in genetic programming, vol. 1. MIT Press (1994)

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

  • Macskassy, S., Provost, F.: Classification in networked data: a toolkit and a univariate case study. J. Mach. Learn. Res. 8, 935–983 (2007)

    Google Scholar 

  • Mariani, M., Baggio, R., Fuchs, M., Höepken, W.: Business intelligence and big data in hospitality and tourism: a systematic literature review. Int. J. Contemp. Hosp. Manag. 30, 3514–3554 (2018)

    Article  Google Scholar 

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

  • Moreno, A., Valls, A., Isern, D., Marin, L., BorríS, J.: Sigtur/e-destination: ontology-based personalized recommendation of tourism and leisure activities. Eng. Appl. Artif. Intell. 26(1), 633–651 (2013)

    Article  Google Scholar 

  • Nemade, G., Deshmane, R., Thakare, P., Patil, M., Thombre, V.D.: Smart tourism recommender system. Int. Res. J. Eng. Technol. (IRJET) 4(11), 601–603 (2017)

    Google Scholar 

  • Nguyen, H., Richards, R., Chan, C.C., Liszka, K.J.: RedTweet: recommendation engine for reddit. J. Intell. Inf. Syst. 47(2), 247–265 (2016)

    Article  Google Scholar 

  • Nilashi, M., Dalvi-Esfahani, M., Roudbaraki, M.Z., Ramayah, T., Ibrahim, O.: A multi-criteria collaborative filtering recommender system using clustering and regression techniques. J. Soft Comput. Decis. Support Syst. 3(5), 24–30 (2016)

    Google Scholar 

  • Noor, N.M.M., Ahm, I.A., Ali, N.H., Ismail. F.: Intelligent Decision Support System for Tourism Destination Choice: A Preliminary Study. International Symposium on Information Technology, Kuala Lumpur (2010)

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

    Article  Google Scholar 

  • Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. The Adaptive Web, pp. 325–341. Springer, Berlin (2007)

    Google Scholar 

  • Pecli, A., Giovanini, B., Pacheco, C.C., Moreira, C., Ferreira, F., Tosta, F., Tesolin, J., Dias, M.V., Filho, S., Cavalcanti, M.C., Goldschmidt, R.: Dimensionality reduction for supervised learning in link prediction problems. In: Proceedings of the 17th International Conference on Enterprise Information Systems, SCITEPRESS—Science and and Technology Publications, pp. 295–302 (2015)

  • Power, D.: What is a DSS? Online Executive J. Data-Intensive Decis. Support 1(3), 223–232 (1997)

    Google Scholar 

  • Power, D.J.: Decision Support Systems: Concepts and Resources for Managers. Quorum Books, Westport (2002)

    Google Scholar 

  • Pozzi, F.A., Fersini, E., Messina, E.: Bayesian model averaging and model selection for polarity classification. In: International Conference on Application of Natural Language to Information Systems. Springer, pp. 189–200 (2013)

  • Pozzi, F.A., Fersini, E., Messina, E., Liu, B.: Sentiment Analysis in Social Networks. Morgan Kaufmann, Boston (2016)

    Google Scholar 

  • Pronoza, E., Yagunova, E., Volskaya, S.: Aspect-based restaurant information extraction for the recommendation system. In: Human Language Technology. Challenges for Computer Science and Linguistics. Springer, pp. 371–385 (2016)

  • Ruotsalo, T., Haav, K., Stoyanov, A., Roche, S., Fani, E., Deliai, R., MäKelä, E., Kauppinen, T., HyvöNen, E.: SmartMuseum: a mobile recommender system for the web of data. Web Semant. 20, 50–67 (2013)

    Article  Google Scholar 

  • Sana, A.A., Philips, J., Tabrizi, N.: Current trends in collaborative filtering recommendation systems. In: Xia, Y., Zhang, L.J. (eds.) Services–SERVICES 2019 Lecture Notes in Computer Science, vol. 11517, pp. 46–60. Springer, Cham (2019)

    Google Scholar 

  • Santiago, F.M., López, F.A., Montejo-Ráez, A., López, A.U.: GeOasis: a knowledge-based geo-referenced tourist assistant. Expert Syst. Appl. 39(14), 11737–11745 (2012)

    Article  Google Scholar 

  • Sebastia, L., Garcia, I., Onaindia, E., Guzman, C.: 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 (2008)

  • Souffriau, W., Vansteenwegen, P.: (2010) Tourist trip planning functionalities: state-of-the-art and future. In: Daniel, F., Facca, F.M. (eds.) Current Trends in Web Engineering ICWE: Lecture Notes in Computer Science. Springer, Berlin (2010)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zhai, H., Li, J.: Refine social relations and differentiate the same friends’ influence in recommender system. In: Mining Intelligence and Knowledge Exploration. Springer, pp. 504–514 (2015)

Download references

Acknowledgements

This work has been partially funded by MISE (Ministero Italiano dello Sviluppo Economico) under the project “SMARTCAL – Smart Tourism in Calabria” (F/050142/01-03/ x32).

Author information

Authors and Affiliations

  1. Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Milan, Italy

    Annarita De Maio, Elisabetta Fersini & Enza Messina

  2. ITACA s.r.l., Rende, CS, Italy

    Francesco Santoro

  3. Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Benevento, Italy

    Antonio Violi

Authors
  1. Annarita De Maio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elisabetta Fersini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enza Messina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesco Santoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Antonio Violi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonio Violi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Maio, A., Fersini, E., Messina, E. et al. Exploiting social data for tourism management: the SMARTCAL project. Qual Quant 57 (Suppl 3), 307–319 (2023). https://doi.org/10.1007/s11135-020-01049-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11135-020-01049-8

Keywords

Search

Navigation