Interconnecting Objects, Visitors, Sites and (Hi)Stories Across Cultural and Historical Concepts: The CrossCult Project

  • Costas Vassilakis
  • Angeliki Antoniou
  • George Lepouras
  • Manolis Wallace
  • Ioanna Lykourentzou
  • Yannick Naudet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10058)


Human History, is a huge mesh of interrelated facts and concepts, spanning beyond borders, encompassing global aspects and finally constituting a shared, global experience. This is especially the case regarding European history, which is highly interconnected by nature; however, most History-related experiences that are today offered to the greater public, from schools to museums, are siloed. The CrossCult project aims to provide the means for offering citizens and cultural venue visitors a more holistic view of history, in the light of cross-border interconnections among pieces of cultural heritage, other citizens viewpoints and physical venues. To this end, the CrossCult project will built a comprehensive knowledge base encompassing information and semantic relationships across cultural information elements, and will provide the technological means for delivering the contents of this knowledge base to citizens and venue visitors in a highly personalized manner, creating narratives for the interactive experiences that maximise situational curiosity and serendipitous learning. The CrossCult platform will also exploit the cognitive/emotional profiles of the participants as well as temporal, spatial and miscellaneous features of context, including holidays and anniversaries, social media trending topics and so forth.


Adaptation User profiles Mobile applications 



Part of this work has been funded by the CrossCult H2020 project, Grant #693150.


  1. 1.
    Antoniou, A., Lepouras, G.: A study to investigate adaptation aspects for museum learning technologies. J. Comput. Cult. Herit. 3(2), Article 7, October 2010Google Scholar
  2. 2.
    Naudet, Y., Antoniou, A., Lykourentzou, I., Tobias, E., Rompa, J., Lepouras, G.: Museum personalization based on gaming and cognitive styles: the BLUE experiment. Int. J. Virtual Communities Soc. Netw. 7(2), 1–29 (2015). Special Issue on Social Media and Networks for Multimedia Content ManagementCrossRefGoogle Scholar
  3. 3.
    Aoidh, E.M., Bertolotto, M., Wilson, D.: Towards dynamic behavior-based profiling for reducing spatial information overload in map browsing activity. GeoInformatica 16, 409–434 (2012)CrossRefGoogle Scholar
  4. 4.
    Ardissono, L., Kufli, T., Petrelli, D.: Personalization in cultural heritage: the road travelled and the one ahead. User Model. User-Adap. Inter. 22(1–2), 73–99 (2012)CrossRefGoogle Scholar
  5. 5.
    Berglund, S., Ekman, J., Deegan-Krause, K., Knuten, T. (eds.): The Handbook of Political Change in Eastern Europe. Edward Elgar Publishing, Cheltenham (2013)Google Scholar
  6. 6.
    Burton, A.: Ten Design Principles. Duke University Press, Durham (2011)Google Scholar
  7. 7.
    Findlater, L., McGrenere, J.: A comparison of static, adaptive, and adaptable menus. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004), pp. 89–96. ACM, New York (2004)Google Scholar
  8. 8.
    Fosh, L., Benford, S., Reeves, S., Koleva, B., Brundell, P.: See me, feel me, touch me, hear me: trajectories and interpretation in a sculpture garden. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), pp. 149–158. ACM, New York (2013).
  9. 9.
    Hameed, M.A., Ramachandram, S., Al Jadaan, O.: Information Gain Clustering Through Prototype-Embedded Genetic K-Mean Algorithm (IGCPGKA): a novel heuristic approach for personalisation of cold start problem. In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology (CCSEIT 2012), pp. 390–395. ACM, New York (2012)Google Scholar
  10. 10.
    Kuik, T., Stock, O., Zancanaro, M., Gorfinkel, A., Jbara, S., Kats, S., Sheidin, J., Kashtan, N.: A visitor’s guide in an active museum: presentations, communications, and reection. J. Comput. Cult. Herit. 3(3), Article 11, February 2011. 25 pages,
  11. 11.
    Lin, C., Xie, R., Li, L., Huang, Z., Li, T.: PRemiSE: personalized news recommendation via implicit social experts. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), pp. 1607–1611. ACM, New York (2012)Google Scholar
  12. 12.
    Lykourentzou, I., Claude, X., Naudet, Y., Tobias, E., Antoniou, A., Lepouras, G., Vassilakis, C.: Improving museum visitors’ quality of experience through intelligent recommendations: a visiting style-based approach. In: Intelligent Environments (Workshops), pp. 507–518 (2013)Google Scholar
  13. 13.
    Naudet, Y., Lykourentzou, I., Tobias, E., Antoniou, A., Rompa, J., Lepouras, G.: Gaming and cognitive profiles for recommendations in museums. In: SMAP 2013, pp. 67–72 (2013)Google Scholar
  14. 14.
    Petrelli, D., Not, E.: User-centred design of flexible hypermedia for a mobile guide: reflections on the HyperAudio experience. User Model. User-Adap. Inter. 15(3–4), 303–338 (2005)CrossRefGoogle Scholar
  15. 15.
    Roussou, M., Katifori, A., Pujol, L., Vayanou, M., Rennick-Egglestone, S.J.: A life of their own: museum visitor personas penetrating the design lifecycle of a mobile experience. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2013), pp. 547–552. ACM, New York (2013)Google Scholar
  16. 16.
    Theodoridis, T., Papadopoulos, S., Kompatsiaris, Y.: Assessing the reliability of Facebook user profiling. In: Proceedings of the 24th International Conference on World Wide Web (WWW 2015 Companion), pp. 129–130. ACM, New York (2015)Google Scholar
  17. 17.
    Tkalcic, M., Chen, L.: Personality and recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 715–739. Springer, New York (2015)CrossRefGoogle Scholar
  18. 18.
    Vayanou, M., Katifori, V., Chrysanthi, A., Antoniou, A.: How to coordinate visitor actions? In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, San Francisco, 27 February–2 March 2016Google Scholar
  19. 19.
    Walczak, K., Wojciechowski, R., Cellary, W.: Dynamic interactive VR network services for education. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology (VRST 2006), pp. 277–286. ACM, New York (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Costas Vassilakis
    • 1
  • Angeliki Antoniou
    • 1
  • George Lepouras
    • 1
  • Manolis Wallace
    • 2
  • Ioanna Lykourentzou
    • 3
  • Yannick Naudet
    • 3
  1. 1.Human-Computer Interaction and Virtual Reality Lab, Department of Informatics and TelecommunicationsUniversity of the PeloponneseTripolisGreece
  2. 2.Knowledge and Uncertainty Research Laboratory, Department of Informatics and TelecommunicationsUniversity of the PeloponneseTripolisGreece
  3. 3.Luxembourg Institute of Science and TechnologyEsch/AlzetteLuxembourg

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