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Cultural heritage visits supported on visitors’ preferences and mobile devices

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Abstract

Monuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general users

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Notes

  1. \(\beta\) is not used in Method A.

References

  1. Akiki, P.A., Bandara, A.K., Yu, Y.: Adaptive model-driven user interface development systems. ACM Comput. Surv. 47(1), 9:1–9:33 (2014). https://doi.org/10.1145/2597999

    Article  Google Scholar 

  2. Baca, M., Gill, M.: Encoding multilingual knowledge systems in the digital age: the Getty vocabularies. Knowl. Organ. 42(4), 232–243 (2015)

    Article  Google Scholar 

  3. Balance: The balance: fine art museum apps for smart phones (2017). https://goo.gl/WEsQRN. Retrieved 13 Sept 2017

  4. Benouaret, I., Lenne, D.: Combining Semantic and Collaborative Recommendations to Generate Personalized Museum Tours, pp. 477–487. Springer, Berlin (2015). https://doi.org/10.1007/978-3-319-23201-0_48

    Book  Google Scholar 

  5. Candlin, F.: The dubious inheritance of touch: art history and museum access. J. Vis. Cult. 5(2), 137–154 (2006). https://doi.org/10.1177/1470412906066906

    Article  Google Scholar 

  6. Cardoso, P., Jesus, M., Márquez, A.: \(\epsilon\) - DANTE : an ant colony oriented depth search procedure. Soft Comput. 15(1), 149–182 (2011). https://doi.org/10.1007/s00500-010-0543-9

    Article  Google Scholar 

  7. Cardoso, P.J.S., Rodrigues, J.M.F., Pereira, J.A.R., Sardo, J.D.P.: An Object Visit Recommender Supported in Multiple Visitors and Museums, pp. 301–312. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58706-6_24

    Book  Google Scholar 

  8. CEUD: Centre for excellence in universal design (2016). http://goo.gl/fUHPj6. Retrieved 13 Sept 2017

  9. CHESS: CHESS - cultural heritage experiences through socio-personal interactions and storytelling (2017). http://www.chessexperience.eu/. Retrieved 13 Sept 2017

  10. CM: Multitouch wall Cleveland Museum (2013). https://goo.gl/kI2oCh. Retrieved 13 Sept 2017

  11. Conati, C,. Carenini, G., Toker, D., Lallé, S.: Towards user-adaptive information visualization. In: AAAI, pp. 4100–4106 (2015)

  12. Coughlan, T., Carletti, L., Giannachi, G., Benford, S., McAuley, D., Price, D., Locatelli, C., Sinker, R., Stack, J.: Artmaps: Interpreting the spatial footprints of artworks. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, CHI ’15, pp. 407–416 (2015). https://doi.org/10.1145/2702123.2702281

  13. Couprie, L.D.: Iconclass, a device for the iconographical analysis of art objects. Mus. Int. (Ed. Fr.) 30(3–4), 194–198 (1978)

    Article  Google Scholar 

  14. Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6(6), 791–812 (1958)

    Article  MathSciNet  Google Scholar 

  15. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008

    Article  Google Scholar 

  16. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  17. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  Google Scholar 

  18. Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)

    Article  Google Scholar 

  19. Gajos, K., Weld, DS.: Supple: automatically generating user interfaces. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 93–100. ACM (2004)

  20. Gajos, K.Z., Wobbrock, J.O., Weld, D.S.: Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces. In: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1257–1266. ACM (2008)

  21. Garcia, I., Sebastia, L., Onaindia, E.: On the design of individual and group recommender systems for tourism. Expert Syst. Appl. 38(6), 7683–7692 (2011). https://doi.org/10.1016/j.eswa.2010.12.143

    Article  Google Scholar 

  22. García-Martínez, C., Cordón, O., Herrera, F.: An empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. In: ANTS Workshop, Lecture Notes in Computer Science, vol. 3172, pp. 61–72 (2004)

  23. Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014). https://doi.org/10.1016/j.jnca.2013.04.006

    Article  Google Scholar 

  24. GeoNames: GeoNames (2017). Retrieved 13 Sept 2017 http://www.geonames.org/

  25. Getty: Getty (2017). Retrieved 13 Sept 2017 http://www.getty.edu/

  26. Gnome: User interface guidelines for supporting accessibility (2016). Retrieved 13 Sept 2017 https://goo.gl/xGAH6f

  27. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, 2008. ICDM’08, pp. 263–272. IEEE (2008)

  28. Iconclass: Iconclass (2017). Retrieved 13 Sept 2017 http://www.iconclass.nl

  29. Isemann, D., Ahmad, K.: Ontological access to images of fine art. J. Comput. Cult. Herit. 7(1), 3:1–3:25 (2014). 10.1145/2538030

    Article  Google Scholar 

  30. IW: InformationWeek: 10 fantastic iphone, android apps for museum visits (2015). https://goo.gl/rYnhm2. Retrieved 13 Sept 2017

  31. Jung, T., Chung, N., Leue, M.C.: The determinants of recommendations to use augmented reality technologies: the case of a korean theme park. Tour. Manag. 49, 75–86 (2015)

    Article  Google Scholar 

  32. Karaman, S., Bagdanov, A.D., Landucci, L., D’Amico, G., Ferracani, A., Pezzatini, D., Del Bimbo, A.: Personalized multimedia content delivery on an interactive table by passive observation of museum visitors. Multimed. Tools Appl. 75(7), 3787–3811 (2016)

    Article  Google Scholar 

  33. Kralisch, A., Eisend, M., Berendt, B.: Impact of culture on website navigation behaviour. In: Proceedings of the HCI-International (2005)

  34. Lindgaard, G., Fernandes, G., Dudek, C., Brown, J.: Attention web designers: you have 50 milliseconds to make a good first impression!. Behav. Inf. Technol. 25(2), 115–126 (2006)

    Article  Google Scholar 

  35. Lohr, S.: Netflix awards \$1 million prize and starts a new contest. New York Times 21 (2009)

  36. Metallinou, A., Wollmer, M., Katsamanis, A., Eyben, F., Schuller, B., Narayanan, S.: Context-sensitive learning for enhanced audiovisual emotion classification. IEEE Trans. Affect. Comput. 3(2), 184–198 (2012)

    Article  Google Scholar 

  37. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Dordrecht (1999)

    MATH  Google Scholar 

  38. Mohan, B.C., Baskaran, R.: A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 39(4), 4618–4627 (2012). https://doi.org/10.1016/j.eswa.2011.09.076

    Article  Google Scholar 

  39. Morency, L.P., Mihalcea, R., Doshi, P.: Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 169–176. ACM (2011)

  40. Palumbo, F., Barsocchi, P., Chessa, S., Augusto, J.C.: A stigmergic approach to indoor localization using bluetooth low energy beacons. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2015)

  41. Pereira, J., Sardo, J., Freitas, M., Veiga, R., Cardoso, P., Rodrigues, J.: Mirar: Mobile image recognition based augmented reality framework. In: International Congress on Engineering and Sustainability in the XXI Century (2017)

  42. Qualcomm: Invisible museum (2016). https://goo.gl/aS0NKh. Retrieved 13 Sept 2017

  43. Reinecke, K., Bernstein, A.: Knowing what a user likes: a design science approach to interfaces that automatically adapt to culture. MIS Q. 37(2), 427–453 (2013)

    Article  Google Scholar 

  44. Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer, Berlin (2015)

    MATH  Google Scholar 

  45. Rodrigues, J., Lessa, J., Gregório, M., Ramos, C., Cardoso, P.: An initial framework for a museum application for senior citizens. In: Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (2016)

  46. Rodrigues, J.M.F., Pereira, J.A.R., Sardo, J.D.P., de Freitas, M.A.G., Cardoso, P.J.S., Gomes, M., Bica, P.: Adaptive Card Design UI Implementation for an Augmented Reality Museum Application. LNCS, vol. 10277, pp. 433–443. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58706-6_35

    Book  Google Scholar 

  47. Route Perfect: Route perfect (2017). https://www.routeperfect.com/. Retrieved 13 Sept 2017

  48. Sardo, J., Semião, J., Monteiro, J., Pereira, J., Freitas, M., Rodrigues, J., Esteves, E.: Portable device for touch, taste and smell sensations in augmented reality experiences (Accepted for the International Congress on Engineering and Sustainability in the XXI cEntury – INCREaSE 2017) (2017)

  49. Schuller, B.W.: Modelling user affect and sentiment in intelligent user interfaces: a tutorial overview. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 443–446. ACM (2015)

  50. SM: Science museum - atmosphere gallery (2016). https://vimeo.com/20789653. Retrieved 13 Sept 2017

  51. Srbija: Srbija 1914 / augmented reality exhibition at historical museum of serbia (2015). https://vimeo.com/126699550. Retrieved 13 Sept 2017

  52. Steichen, B., Conati, C., Carenini, G.: Inferring visualization task properties, user performance, and user cognitive abilities from eye gaze data. ACM Trans. Interact. Intell. Syst. 4(2), 11 (2014)

    Article  Google Scholar 

  53. Unity: Unity 3D (2017). https://unity3d.com/pt. Retrieved 13 Sept 2017

  54. UVAM: Interactive ipad museum catalog (2016). https://vimeo.com/31821923. Retrieved 13 Sept 2017

  55. Vainstein, N., Kuflik, T., Lanir, J.: Towards using mobile, head-worn displays in cultural heritage: user requirements and a research agenda. In: Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 327–331. ACM (2016)

  56. van Hage, W.R., Stash, N., Wang, Y., Aroyo, L.: Finding Your Way Through the Rijksmuseum with an Adaptive Mobile Museum Guide, pp. 46–59. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-13486-9_4

    Book  Google Scholar 

  57. Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003). https://doi.org/10.2307/30036540

    Article  Google Scholar 

  58. Venkatesh, V., Thong, J., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012). https://doi.org/10.2307/41410412

    Article  Google Scholar 

  59. Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012). https://doi.org/10.1109/TLT.2012.11

    Article  Google Scholar 

  60. Wang, D., Xiang, Z.: The New Landscape of Travel: A Comprehensive Analysis of Smartphone Apps, pp. 308–319. Springer, Vienna (2012). https://doi.org/10.1007/978-3-7091-1142-0_27

    Book  Google Scholar 

  61. WSJ: The Wall Street Journal: best apps for visiting museums (2015). https://goo.gl/cPTyP9. Retrieved 13 Sept 2017

  62. Zhao, L., Lu, Y., Zhang, L., Chau, P.Y.: Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decis. Support Syst. 52(3), 645–656 (2012)

    Article  Google Scholar 

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Acknowledgements

We thank our project leader SPIC - Creative Solutions [www.spic.pt] and the Museu Municipal de Faro.

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Authors and Affiliations

  1. LARSyS and ISE, University of the Algarve, Faro, Portugal

    Pedro J. S. Cardoso, João M. F. Rodrigues, João Pereira & Roman Bajireanu

  2. ISE, University of the Algarve, Faro, Portugal

    Sergey Nogin

  3. CIAUD and ESEC, University of the Algarve, Faro, Portugal

    Joana Lessa

  4. CIEO and ESGHT, University of the Algarve, Faro, Portugal

    Célia M. Q. Ramos

  5. SPIC - Creative Solutions, Loulé, Portugal

    Miguel Gomes & Paulo Bica

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  1. Pedro J. S. Cardoso
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  2. João M. F. Rodrigues
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Correspondence to João M. F. Rodrigues.

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This work was supported by the Portuguese Foundation for Science and Technology (FCT) projects LARSyS (UID/EEA/50009/2013) and CIEO (UID/SOC/04020/2013), and project M5SAR I&DT nr. 3322 financed by CRESC ALGARVE 2020, PORTUGAL 2020 and FEDER.

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Cardoso, P.J.S., Rodrigues, J.M.F., Pereira, J. et al. Cultural heritage visits supported on visitors’ preferences and mobile devices. Univ Access Inf Soc 19, 499–513 (2020). https://doi.org/10.1007/s10209-019-00657-y

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