Utilising Crowd Information of Tourist Spots in an Interactive Tour Recommender System

  • Takashi AoikeEmail author
  • Bach Ho
  • Tatsunori Hara
  • Jun Ota
  • Yohei Kurata
Conference paper


Although the congestion of tourist spots has a huge effect on tourist experiences, few studies have discussed crowd information in the research field of recommender systems for tour planning. This study developed a recommender system that utilises crowd information interactively to support tour planning. The study created a bar graph about relative crowdedness in a day based on the idea that the measures required for a crowd vary depending on each tourist. This research conducted user experiments to examine how tourists are conscious of crowds. The proposed system can provide alternative plans in 70% of cases when tourists wish to visit a spot when it is not crowded. Furthermore, the results imply the importance of focusing on differences in tourists with regard to a sightseeing spot. The sightseeing experiences of tourists may be enhanced by conducting expectation management for sightseeing using ICT.


Recommender system Service design Crowding data FIT 



The authors are grateful to Fujitsu Laboratories Ltd. for assistance with the experiment.


  1. 1.
    Kasahara, S., Iiyama, M., Minoh, M.: Regional data distribution using tourism service portfolio. In Information and Communication Engineers, Technical Report of IEICE, pp. 1–6 (2017)Google Scholar
  2. 2.
    Mowen A, Vogelsong H, Grafe A (2003) Perceived crowding and its relationship to crowd management practices at park and recreation events. Event Manag 8(2):63–72CrossRefGoogle Scholar
  3. 3.
    Wickham T, Kerstetter D (2000) The relationship between place attachment and crowding in an event setting. Event Manag 6(3):167–174Google Scholar
  4. 4.
    Vansteenwegen P, Souffriau W, Vanden Berghe G, Van Oudheusden D (2011) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546CrossRefGoogle Scholar
  5. 5.
    Navío-Marco J, Ruiz-Gómez LR, Sevilla-Sevilla C (2018) Progress in information technology and tourism management: 30 years on and 20 years after the internet—revisiting Buhalis & Law’s landmark study about eTourism progress. Tour Manag 69:460–470CrossRefGoogle Scholar
  6. 6.
    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. J Heuristics 20(3):291–328CrossRefGoogle Scholar
  7. 7.
    Lim, K.H.: Personalized tour recommendation using location-based social media. Ph.D. Thesis (2017)Google Scholar
  8. 8.
    Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N, Zaroliagis C (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42(21):7303–7316CrossRefGoogle Scholar
  9. 9.
    Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N (2017) Scenic route planning for tourists. Pers Ubiquitous Comput 21(1):137–155CrossRefGoogle Scholar
  10. 10.
    Golden BL, Levy L, Vohra R (1987) The orienteering problem. Nav Res Logist 34(3):307–318CrossRefGoogle Scholar
  11. 11.
    Gunawan A, Lau HC, Vansteenwegen P (2016) Orienteering problem: a survey of recent variants, solution approaches and applications. Eur J Oper Res 255(2):315–332CrossRefGoogle Scholar
  12. 12.
    Alghamdi, H., Zhu, S., El Saddik, A.: E-tourism: mobile dynamic trip planner. In: 2016 IEEE International Symposium on Multimedia (ISM), pp. 185–188. IEEE (2016)Google Scholar
  13. 13.
    Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: a case-based travel advisory system. Lecture Notes in Computer Science, vol. 2416, pp. 613–627 (2002)Google Scholar
  14. 14.
    Lee, J., Kang, E., Park, G.: Design and implementation of a tour planning system for telematics users. In: ICCSA 2007, pp. 179–189. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Maruyama A, Shibata N, Murata Y, Yasumoto K, Ito M (2004) A personal tourism navigation system to support traveling multiple destinations with time restrictions. Adv Inf Netw Appl 2004(2):18–21Google Scholar
  16. 16.
    Roy, S., Das, G., Amer-Yahia, S., Yu, C.: Interactive itinerary planning. In: IEEE 27th international conference, pp. 15–26 (2011)Google Scholar
  17. 17.
    Borras J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389CrossRefGoogle Scholar
  18. 18.
    Pessemier, T., Dhondt, J., Vanhecke, K., Martens, L.: TravelWithFriends: a hybrid group recommender system for travel destinations. In: Proceedings of workshop on tourism recommender systems, RecSys15, pp. 51–60 (2015)Google Scholar
  19. 19.
    Yang WS, Hwang SY (2013) iTravel: a recommender system in mobile peer-to-peer environment. J Syst Softw 86(1):12–20CrossRefGoogle Scholar
  20. 20.
    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370CrossRefGoogle Scholar
  21. 21.
    Manouselis N, Costopoulou C (2007) Analysis and classification of multicriteria recommender systems. World Wide Web 10(4):415–441CrossRefGoogle Scholar
  22. 22.
    Montaner M, López B, de la Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell 19(3):285–330CrossRefGoogle Scholar
  23. 23.
    Sebastia, L., Yuste, D., Garcia, I., Garrido, A., Onaindia, E.: A highly interactive recommender system for multi-day trips. In: Proceedings of workshop on tourism recommender systems, RecSys15, pp. 1–10 (2015)Google Scholar
  24. 24.
    Wang W, Zeng G, Tang D (2011) Bayesian intelligent semantic mashup for tourism. Concurr Comput Pract Exp 23:850–862CrossRefGoogle Scholar
  25. 25.
    Kurata, Y., Hara, T.: CT-planner4: toward a more user-friendly interactive day-tour planner. In: Information and Communication Technologies in Tourism 2014, pp. 73–86. Springer International Publishing (2013)Google Scholar
  26. 26.
    Karanikolaou, S., Boutsis, I., Kalogeraki, V.: Understanding event attendance through analysis of human crowd behavior in social networks. In: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems, pp. 322–325. ACM (2014)Google Scholar
  27. 27.
    Zhang, L., Wang, Y.P., Sun, J., Yu, B.: The sightseeing bus schedule optimization under park and ride systems in tourist attractions. Ann Oper Res 1–19 (2016).
  28. 28.
    Hasuike T, Katagiri H, Tsubaki H, Tsuda H (2013) Tour planning for sightseeing with time-dependent satisfactions of activities and traveling times. Am J Oper Res 3(3):369–379CrossRefGoogle Scholar
  29. 29.
    Kuriyama H, Murata Y, Shibata N, Yasumoto K (2010) Simultaneous multi-user scheduled cyclic scheduling method considering congestion situation in cities and tourist spots. Inf Process Soc Jpn Trans Inf Process Soc Jpn 51(3):885–898Google Scholar
  30. 30.
    Popp M (2012) Positive and negative urban tourist crowding: Florence, Italy. Tour Geogr 14(1):50–72CrossRefGoogle Scholar
  31. 31.
    Laporte G, Martello S (1990) The selective travelling salesman problem. Discrete Appl Math 26(2):193–207CrossRefGoogle Scholar
  32. 32.
    Google My Business Help. Popular times, wait times, and visit duration. Accessed 10 Sep 2018
  33. 33.
    Bosque IR, Martin HS (2008) Tourist satisfaction:a cognitive-affective model. Ann Tourism Res 35:551–573CrossRefGoogle Scholar
  34. 34.
    Kurata, Y., Shinagawa, Y., Hara, T.: CT-Planner5: a computer-aided tour planning service which profits both tourists and destinations. In: Proceedings of the workshop on tourism recommender systems in 9th ACM conference on recommender systems (RecSys 2015), 35–42 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Takashi Aoike
    • 1
    Email author
  • Bach Ho
    • 1
  • Tatsunori Hara
    • 1
  • Jun Ota
    • 1
  • Yohei Kurata
    • 2
  1. 1.Research into Artifacts, Center for Engineering (RACE), The University of TokyoTokyoJapan
  2. 2.Department of Tourism Science, Graduate School of Urban Environmental SciencesTokyo Metropolitan UniversityTokyoJapan

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