Abstract
This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
World Tourism Organization UNWTO. UNWTO Tourism Highlights: 2018 Edition.
- 2.
- 3.
- 4.
- 5.
Comité Régional du Tourisme Paris Ile-de-France. “Clientèle russe Repères” for the years 2011 to 2015.
References
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM (2002)
Chalfen, R.M.: Photograph’s role in tourism: some unexplored relationships. Ann. Tourism Res. 6(4), 435–447 (1979)
Chareyron, G., Cousin, S., Da-Rugna, J., Gabay, D.: Touriscope: map the world using geolocated photographies. In: IGU Meeting, Geography of Tourism, Leisure and Global Change (2009)
Chareyron, G., Da-Rugna, J., Raimbault, T.: Big data: a new challenge for tourism. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 5–7. IEEE (2014)
Cooper, C., Michael Hall, C.: Contemporary Tourism. Routledge (2007)
Da Rugna, J., Chareyron, G., Branchet, B.: Tourist behavior analysis through geotagged photographies: a method to identify the country of origin. In: 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 347–351. IEEE (2012)
Fournier-Viger, P., Gomariz, A., Gueniche, T., Mwamikazi, E., Thomas, R.: TKS: efficient mining of Top-K sequential patterns. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8346, pp. 109–120. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53914-5_10
Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V.S.: ERMiner: sequential rule mining using equivalence classes. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds.) IDA 2014. LNCS, vol. 8819, pp. 108–119. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12571-8_10
Fournier-Viger, P., Chun-Wei Lin, J., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)
Fournier-Viger, P., Nkambou, R., Shin-Mu Tseng, V.: RuleGrowth: mining sequential rules common to several sequences by pattern-growth. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 956–961. ACM (2011)
Fournier-Viger, P., Tseng, V.S.: TNS: mining top-k non-redundant sequential rules. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 164–166. ACM (2013)
Fei, H., Li, Z., Yang, C., Jiang, Y.: A graph-based approach to detecting tourist movement patterns using social media data. Cartography Geograph. Inf. Sci. 46(4), 368–382 (2019)
Li, J., Lizhi, X., Tang, L., Wang, S., Li, L.: Big data in tourism research: a literature review. Tour. Manag. 68, 301–323 (2018)
March, R., Woodside, A.G.: Tourism Behaviour: Travellers’ Decisions and Actions. CABI, Wallingford (2005)
Pei, J., et al.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)
Soulet, A.: Two decades of pattern mining: principles and methods. In: Marcel, P., Zimányi, E. (eds.) eBISS 2016. LNBIP, vol. 280, pp. 59–78. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61164-8_3
Talpur, A., Zhang, Y.: A study of tourist sequential activity patterns through location based social network (LBSN). arXiv preprint arXiv:1811.03426 (2018)
Versichele, M., De Groote, L., Bouuaert, M.C., Neutens, T., Moerman, I., Van de Weghe, N.: Pattern mining in tourist attraction visits through association rule learning on bluetooth tracking data: a case study of Ghent. Belgium. Tourism Manage. 44, 67–81 (2014)
Yang, L., Lun, W., Liu, Y., Kang, C.: Quantifying tourist behavior patterns by travel motifs and geo-tagged photos from flickr. ISPRS Int. J. Geo-Inf. 6(11), 345 (2017)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)
Zhao, Q., Bhowmick, S.S.: Sequential pattern mining: a survey. ITechnical Report CAIS Nayang Technological University Singapore, 1:26 (2003)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ben Baccar, L., Djebali, S., Guérard, G. (2019). Tourist’s Tour Prediction by Sequential Data Mining Approach. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)