Context-Aware Generation of Personalized Audio Tours: Approach and Evaluation

  • Nikolay Shilov
  • Alexey Kashevnik
  • Sergey Mikhailov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)


Development of information and communication technologies together with growing popularity of e-tourism open boundaries for new concepts and approaches. Automated tours is one of such directions. There exist a number of approaches aimed at selecting a particular tour based on the analysis of certain parameters. In the paper, the authors consider an approach to generation of audio tours from available fragments based on the analysis of the context and tourists’ preferences. This makes it possible for the tours to be very flexible and adjustable “on-the-go” based on the tourist feedback or actions. The approach is evaluated through an experiment that has shown a high level of acceptance of the generated tours by people. Future work is aimed at implementation and evaluation of the feedback analysis to improve the system. It is suggested to replace explicit feedback with analysis of tourist’s actions like skipping some fragments or searching for more information.


Tour Context Personalization Preferences Approach Evaluation 



The paper is partially due to the project sponsored by the Ford University Research Program, State Research # 0073-2018-0002, and projects funded by grants # 18-07-01201 and 17-29-03284 of the Russian Foundation for Basic Research. The work has been also partially financially supported by the Government of Russian Federation, Grant 08-08.


  1. 1.
    Smirnov, A., Shilov, N., Gusikhin, O.: Cyber-physical-human system for connected car-based e-tourism: approach and case study scenario. In: 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017 (2017)Google Scholar
  2. 2.
    Giuseppe, M., et al.: QRCODE and RFID integrated technologies for the enhancement of museum collections. In: Ioannides, M., M-T, N., Fink, E., Žarnić, R., Yen, A.-Y., Quak, E. (eds.) EuroMed 2014. LNCS, vol. 8740, pp. 759–766. Springer, Cham (2014). Scholar
  3. 3.
    Li, R.Y.-C., Liew, A.W.-C.: An interactive user interface prototype design for enhancing on-site museum and art gallery experience through digital technology. Mus. Manag. Curatorsh. 30, 208–229 (2015)CrossRefGoogle Scholar
  4. 4.
    Modsching, M., Kramer, R., Ten Hagen, K., Gretzel, U.: Effectiveness of mobile recommender systems for tourist destinations: a user evaluation. In: 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 663–668. IEEE (2007)Google Scholar
  5. 5.
    Ambrosino, G., Boero, M., Nelson, J.D., Romanazzo, M. (eds.): Infomobility Systems and Sustainable Transport Services. ENEA, Rome (2010)Google Scholar
  6. 6.
  7. 7.
    Cowen, B.: A personal tour guide – almost everywhere – for $9.99 or less!.
  8. 8.
    Smirnov, A., Shilov, N., Gusikhin, O.: Socio-cyberphysical system for proactive driver support: approach and case study. In: Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2015, pp. 289–295 (2015)Google Scholar
  9. 9.
    Smirnov, A., Kashevnik, A., Ponomarev, A., Shilov, N., Teslya, N.: Proactive recommendation system for m-tourism application. In: Johansson, B., Andersson, B., Holmberg, N. (eds.) BIR 2014. LNBIP, vol. 194, pp. 113–127. Springer, Cham (2014). Scholar
  10. 10.
    Gerhardt, T.: 3 Ways Multi-Modal Travel is Tricky for App Developers.
  11. 11.
    Staab, S., et al.: Intelligent systems for tourism. IEEE Intell. Syst. 17, 53–66 (2002)CrossRefGoogle Scholar
  12. 12.
    Hasuike, T., Katagiri, H., Tsubaki, H., Tsuda, H.: Interactive approaches for sightseeing route planning under uncertain traffic and ambiguous tourist’s satisfaction. In: Eto, H. (ed.) New Business Opportunities in the Growing E-Tourism Industry, pp. 75–96. IGI Global, Hershey (2015)CrossRefGoogle Scholar
  13. 13.
    Dey, A.K.: Understanding and Using Context. Pers. Ubiquitous Comput. 5, 4–7 (2001)CrossRefGoogle Scholar
  14. 14.
    Tomko, M., Chairs, K.R.W.: AGILE Workshop on Adaptation in Spatial Communication (2009)Google Scholar
  15. 15.
    Marzal, E., Ibanez, J., Sebastia, L., Onaindia, E.: Temporal goal reasoning for predictive performance of a tourist application. In: Proceedings of the 2nd Workshop on Artificial Intelligence and Internet of Things co-located with the 22nd European Conference on Artificial Intelligence (ECAI 2016), pp. 7–14. CEUR (2016)Google Scholar
  16. 16.
    Ghosh, A., Patel, Y., Sukhwani, M., Jawahar, C.V.: Dynamic Narratives for Heritage Tour. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 856–870. Springer, Cham (2016). Scholar
  17. 17.
    Li, S., Duan, X., Bai, Y., Yun, C.: Development and application of intelligent tour guide system in mobile terminal. In: 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, pp. 383–387. IEEE (2015)Google Scholar
  18. 18.
    Oppermann, R., Specht, M.: A context-sensitive nomadic exhibition guide. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 127–142. Springer, Heidelberg (2000). Scholar
  19. 19.
    Sookhanaphibarn, K., Thawonmas, R.: A movement data analysis and synthesis tool for museum visitors’ behaviors. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds.) PCM 2009. LNCS, vol. 5879, pp. 144–154. Springer, Heidelberg (2009). Scholar
  20. 20.
    Huang, H.-H., Cerekovic, A., Pandzic, I.S., Nakano, Y., Nishida, T.: Toward a multi-culture adaptive virtual tour guide agent with a modular approach. AI Soc. 24, 225–235 (2009)CrossRefGoogle Scholar
  21. 21.
    Kramer, R., Modsching, M., Schulze, J., Hermkes, M., ten Hagen, K.: Context driven, adaptive tour computation and information presentation. In: First International Workshop on Managing Context Information in Mobile and Pervasive Environments (2005)Google Scholar
  22. 22.
    Hao, F., Li, S., Min, G., Kim, H.-C., Yau, S.S., Yang, L.T.: An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments. IEEE Trans. Serv. Comput. 8, 520–533 (2015)CrossRefGoogle Scholar
  23. 23.
    Smirnov, A.V., Kashevnik, A.M., Ponomarev, A.: Context-based infomobility system for cultural heritage recommendation: tourist assistant—TAIS. Pers. Ubiquitous Comput. 21, 297–311 (2017)CrossRefGoogle Scholar
  24. 24.
    Smirnov, A., Kashevnik, A., Shilov, N., Teslya, N., Shabaev, A.: Mobile application for guiding tourist activities: tourist assistant - TAIS. In: Conference of Open Innovation Association, FRUCT, pp. 95–100 (2014)Google Scholar
  25. 25.
    Chelaramani, S., Muthireddy, V., Jawahar, C.V.: An Interactive tour guide for a heritage site. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2943–2952. IEEE (2017)Google Scholar
  26. 26.
    Di Bitonto, P., Roselli, T., Rossano, V., Monacis, L., Sinatra, M.: MoMAt: a mobile museum adaptive tour. In: 2010 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, pp. 156–160. IEEE (2010)Google Scholar
  27. 27.
    Sammut, C., Webb, G.I. (eds.): Encyclopedia of Machine Learning. Springer, Boston (2010)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Nikolay Shilov
    • 1
  • Alexey Kashevnik
    • 2
  • Sergey Mikhailov
    • 1
    • 2
  1. 1.SPIIRASSt. PetersburgRussian Federation
  2. 2.ITMO UniversitySt. PetersburgRussian Federation

Personalised recommendations