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An evolving museum metaphor applied to cultural heritage for personalized content delivery

Abstract

The aim of this article concerns adaptive and personalized navigation in a cultural heritage database. The theoretical grounding of the proposition relies on cognitive science, particularly constructivism and enaction. The navigation is conducted via an intelligent interface through a 3D “living” museum metaphor. The purpose of this interface is to recommend dynamic cultural heritage objects according to a user profile that is computed online from the interactions that a user has with these objects. To this end, objects are linked to semantic structures that represent relations between cultural heritage concepts. The user profile is described in terms of cultural heritage interests. A prototype of this principle is used to evaluate some of the basic hypotheses of this proposition.

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Notes

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    The Trevarez castle: https://en.wikipedia.org/wiki/Chateau_de_Trevarez.

References

  1. Aggarwal, R., Grantcharov, T., Moorthy, K., Hance, J., Darzi, A.: A competency-based virtual reality training curriculum for the acquisition of laparoscopic psychomotor skill. Am. J. Surg. 191(1), 128–133 (2006). https://doi.org/10.1016/j.amjsurg.2005.10.014

  2. Albanese, M., Picariello, A., Rinaldi, A.: A technological framework for personalized museum visiting. In: Proceedings of the 10th International Conference on Information Systems Analysis and Synthesis (ISAS 2004) and International Conference on Cybernetics and Information Technologies, Systems and Applications (CITSA 2004), pp. 273–278 (2004)

  3. Athukorale, K., Medlar, A., Oulasvirta, A., Jacucci, G., Glowacka, D.: Beyond relevance: adapting exploration/exploitation in information retrieval. In: 21st Annual Meeting on Intelligent User Interface (IUI 2016). ACM (2016)

  4. Aviles Collao, J., Diaz-Kommonen, L., Kaipainen, M., Pietarila, J.: Soft ontologies and similarity cluster tools to facilitate exploration and discovery of cultural heritage ressources. In: Proceedings of International Cultural Heritage Informatics Meeting (ICHIM 03) (2003)

  5. Azzag, H., Picarougne, F., Guinot, C., Venturini, G.: Vrminer: a tool for multimedia database mining with virtual reality. In: Proceedings of Processing and Managing Complex Data for Decision Support (2005)

  6. Bonhert, F., Zukerman, I.: Using viewing time for theme prediction in cultural heritage spaces. In: Proceedings of Austral Asian joint Conference on Artificial Intelligence AI 2007. Advances in Artificial Intelligence (2007)

  7. Bonis, B., Vosinaki, S., Andreou, I., Panayiotopoulos, T.: Adaptive virtual exhibitions. DESIDOC J. Libr. Inf. Technol. 33(3), 183–198 (2013)

  8. Boyack, K., Wylie, B., Davidson, G.: Domain visualization using vxinsight for science and technology management. J. Am. Soc. Inf. Sci. Technol. 53(9), 764–774 (2002)

  9. Card, S., Robertson, G., York, W.: The webbook and the web forager: An information workspace for the world-wilde web. In: Human Factors in Computer Systems: Proceedings of the CHI’96 Conference. ACM (1996)

  10. Chittaro, L., Ieronutti, L.: A visual tool for tracing users’ behavior in virtual environments. In: AVI (2004)

  11. Damiano, R., Lieto, A.: Visual metaphors for semantic cultural heritage. In: 7th International Conference on Intelligent Technologies for Interactive Entertainment INTETIN, 2015. IEEE (2015)

  12. De Loor, P., Manac’h, K., Tisseau, J.: Enaction-based artificial intelligence: toward co-evolution with humans in the loop. Minds Mach. 19(3), 319–343 (2009). https://doi.org/10.1007/s11023-009-9165-3

  13. Donalek, C., Djorgovski, S., Cioc, A., Wang, A., Zhang, J., Lawler, E., Yeh, S.: Immersive and collaborative data visualization using virtual reality platform. In: Proceedings of IEEE International Conference on Big Data, pp. 609–614 (2014)

  14. Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999). https://doi.org/10.1162/106454699568728

  15. Dörk, M., Williamson, C., Carpendale, S.: Navigating tomorrow’s web: from searching and browsing to visual exploration. ACM Trans. Web (TWEB) 6(3), 13 (2012)

  16. Dörk, M., Carpendale, S., Collins, C., Williamson, C.: Visgets : Coordinated visualizations for web-based information exploration and discovery. In: IEEE Transactions on Visualization and Computer Graphics, vol. 14, pp. 1205–1212. IEEE (2008)

  17. Hoareau, C., Querrec, R., Buche, C., Ganier, F.: Evaluation of internal and external validity of a virtual environment for learning a long procedure. Int. J. Hum. Comput. Interact. 33(10), 786–798 (2017). https://doi.org/10.1080/10447318.2017.1286768

  18. Jennett, C., Cox, A.L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., Walton, A.: Measuring and defining the experience of immersion in games. Int. J. Hum. Comput. Stud. 66(9), 641–661 (2008)

  19. Kaipainen, M., Normak, P., Niglas, K., Kippar, J., Laanpere, M.: Soft ontologies, spatial representation and multi-perspective explorability. Expert Syst. 25(05), 474–483 (2008)

  20. Keim, D.: Information visualization and visual data mining. In: Proceedings of IEEE Transactions on Visualization and Computer Graphics, vol. 8 (2002)

  21. Kelly, D., Fu, X.: Elicitation of term relevance feedback : an investigation of term source and context. SIGIR 2006, 453–460 (2006)

  22. Koutsoudis, A., Makarona, C., Pavlidis, G.: Content-based navigation within virtual museums. J. Adv. Comput. Sci. Technol. 1(2), 73–81 (2012)

  23. Krosnick, J.a., Presser, S.: Question and questionnaire design. http://books.google.com/books?id=mMPDPXpTP-0C&pgis=1 (2010)

  24. Kuflik, T., Boger, Z., Zancanaro, M.: Analysis and Prediction of Museum Visitors’ Behavioral Pattern Types, pp. 161–176. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-27663-7_10

  25. Le patrimoine des communes du Finistère. Flohic (1998)

  26. Li, W., Goodchild, M.F., Raskin, R.: Towards geospatial semantic search: exploiting latent semantic relations in geospatial data. Int. J. Digit. Earth 7(1), 17–37 (2014)

  27. Noë, A.: Action in Perception. A Bradford Book. MIT Press, Cambridge (2004). ISBN: 9780262140881

  28. Nuzzolese, A.G., Presutti, V., Gangemi, A., Peroni, S., Ciancarini, P.: Aemoo: linked data exploration based on knowledge patterns. Semant. Web 8(1), 87–112 (2017)

  29. O’Brien, H.L., Cairns, P., Hall, M.: A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form. Int. J. Hum. Comput. Stud. 112, 28–39 (2018)

  30. Piaget, J.: Play, Dreams and Imitation in Children. Routledge, Abingdon (1951)

  31. Poco, J., Dasgupta, A., Wei, Y., Hargrove, W., Schwalm, C., Cook, R., Bertini, E., Silva, C.: Similarityexplorer: A visual inter-comparison tool for multifaceted climate data. In: Computer Graphics Forum, vol. 33, pp. 341–350. Wiley Online Library (2014)

  32. Robertson, G., Czerwinski, M., Larson, K., Robins, D., D., T., van Dantzich, M.: Data mountain: using spatial memory for document management. In: ACM Symposium on User Interface Software and Technology, pp. 153–162. ACM (1998)

  33. dos Santos, C.T., Osorio, F.S.: Adaptive: an intelligent virtual environment and its application in e-commerce. In: Computer Software and Applications Conference, 2004. COMPSAC 2004. Proceedings of the 28th Annual International, pp. 468–473. IEEE (2004)

  34. Sjöberg, M., Vitaniemi, V., Laaksonen, J., Hokela, T.: Analysis of semantic information available in an image collection augmented with auxiliary data. In: Artificial Intelligence Applications and Innovations, 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) (2006)

  35. Sookhanaphibarn, K., Thawonmas, R.: An on-line classification approach of visitor’s movement in 3d virtual museums. In: Proceedings of the International Federation of Information Processing (IFIP), Advances in Information and Communication Technology (2010)

  36. Sparacino, F.: Sto(ry)chastics : a Bayesian network architecture for combined user modeling, sensor fusion and computational storytelling for interactive spaces (2001)

  37. Sparacino, F., De Vaul, R., Wren, C., Mac Neil, G., Daveport, G., Pentland, A.: City of news. In: SIGGRAPH99, Visual Proceedings, Emerging Technologies (1999)

  38. Stock, O., Zancanaro, M., Busetta, P., Callaway, C., Krüger, A., Kruppa, M., Kuflik, T., Not, E., Rocchi, C.: Adaptive, intelligent presentation of information for the museum visitor in peach. User Modeling and User-Adapted Interaction 17(3), 257–304 (2007)

  39. Sun, H., Jiang, C., Ding, Z., Wang, P., Zhou, M.: Topic-oriented exploratory search based on an indexing network. IEEE Trans. Syst. Man Cybern. Syst. 46(2), 234–247 (2016). https://doi.org/10.1109/TSMC.2015.2421484

  40. Svanaes, D.: Interaction design for and with the lived body: some implications of merleau-ponty’s phenomenology. In: Proceedings of ACM Transactions on Computer-Human Interaction (TOCHI) - Special Issue on the Theory and Practice of Embodied Interaction in HCI and Interaction Design, vol. 20 (2013)

  41. Teras, M., Raghunathan, S.: Big data visualization in immersive virtual reality environments: embodied phenomenological perspectives to interaction. ICTACT J. Soft Comput. 05(04), 1009–1015 (2015)

  42. Varela, F.J., Thompson, E., Rosch, E.: The Embodied Mind. MIT Press, Cambridge (1993)

  43. Veron, E., Levasseur, M.: Bibliothèque Publique d’Information, Centre Georges Pompidou (1983)

  44. Wang, Y., Aroyo, L.M., Stash, N., Rutledge, L.: Interactive user modeling for personalized access to museum collections: the rijksmuseum case study. In: Conati, C., McCoy, K., Paliouras, G. (eds.) User Modeling 2007, pp. 385–389. Springer Berlin Heidelberg, Berlin (2007). ISBN: 978-3-540-73078-1

  45. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)

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Acknowledgements

This work was funded by the ANR (Agence Nationale de la Recherche), Antimoine Project.

Author information

Correspondence to Landy Rajaonarivo.

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Appendix

Appendix

The two next tables provide the description of the collected data during Exp. 1(Table 3) and Exp. 2 (Table 4).

Table 3 Each line corresponds to a participant for Exp.1. Column \(C_1\) is the genre of the participant, \(C_2\) is her age, \(C_3\) her familiarity with video game, \(C_3\) her level of interest for cultural heritage
Table 4 Each line corresponds to a participant for Exp.2. Column C is the order of the conditions (2D then 3D or 3D then 2D), G is the genre of the participant, A is her age, VG her familiarity with video games, CH her level of interest for cultural heritage. Q1 to Q5 is her answer to corresponding questions (see Table 1)

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Rajaonarivo, L., Maisel, E. & De Loor, P. An evolving museum metaphor applied to cultural heritage for personalized content delivery. User Model User-Adap Inter 29, 161–200 (2019). https://doi.org/10.1007/s11257-019-09222-x

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Keywords

  • Visual metaphor
  • Real-time adaptation
  • Profiling techniques
  • Personalized database exploration
  • Virtual museum