Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Incorporating Time Constraints into a Recommender System for Museum Visitors

  • Received : 2019.11.11
  • Accepted : 2020.04.22
  • Published : 2020.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users' previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.

Keywords

References

  1. C. C. Aggarwal, "An introduction to recommender system," Recommender Systems, pp 1-28. Springer, Cham, 2016, DOI: 10.1007/978-3-319-39659-3_1.
  2. R. P. Biuk-Aghai, S. Fong, and Y. Si, "Design of a recommender system for mobile tourism multimedia selection," 2nd International Conference on Internet Multimedia Services Architecture and Applications, Bangalore, pp. 1-6, 2008.
  3. D. Luh and T. Yang, "Museum recommendation system based on lifestyles," 9th International Conference on Computer-Aided Industrial Design and Conceptual Design, Kunming, pp. 884-889, 2008.
  4. I. Roes, N. Stash, Y. Wang, and L. Aroyo, "A personalized walk through the museum," The CHIP Interactive Tour guide, in CHIP 2009 Extended Abstract on Human Factor in Computing System, pp. 3317-3322, ACM, 2009.
  5. I. Lykourentzoy, X. Claude, and Y. Naudet, "Improving museum visitors; quality of experience through intelligent recommendations: a visiting style-based approach," Conference: Museums as intelligent environments (MasIE2013)- Workshop co-located with the 9th International Conference on Intelligent Environments, Athens, Greece, pp. 507-518, 2013, DOI:10.3233/978-1-61499-286-8-507.
  6. R. Fournier, E. Viennet, S. Sean, F.F. Soulie, and M. Benaiche, "AMMICO: social recommendation for museums," in Proceeding of Digital Intelligence, Nantes, France, 2014.
  7. I. Keller and E. Viennet, "Recommender systems for museums: evaluation on a real dataset," in Fifth International Conference on Advances in Information Mining and Management, pp. 65-71, Brussels, Belgium, 2015.
  8. T. Kuflik, E. Minkov, and K. Kahanov, "Graph-based recommendation in the Museum," in CEUR Workshop Proceedings, vol. 1278, Proceedings of the First International Workshop on Decision Making and Recommender Systems, Bolzano, Italy, pp. 46-48, 2014.
  9. G. Pavlidis, "Towards a novel user satisfaction modelling for museum visit recommender systems," in M. Duguleana, M. Carrozzino, M. Gams, and I. Tanea (eds), VR Technologies in Cultural Heritage, VRTCH 2018, Communications in Computer and Information Science, vol. 904. Springer, Cham, pp. 60-75, 2018.
  10. M. C. Rodriguez-Hernandez, S. Ilarri, R. Hermoso, and R. T. Lado, "Towards trajectory-based recommendations in museums: evaluation of strategies using mixed synthetic and real data," The 8th international conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 234-239, 2017.
  11. L. Rajaonaruvo, A. Fonteles, C. Sallberry, M. Bessagnet, P. Roose, P. Etcheverrt, C. Marquesazaa, A. L. P. Lacayrelle, C. Cayere, and Q. Coudert, "Recommendation of heterogeneous cultural heritage objects for promotion of tourism," International Journal of Geoinformation, vol. 8, no. 5, pp. 230-255, 2019, DOI: 10.3390/ijgi8050230.
  12. L. Kovavisaruch, T. Sanpechuda, K. Chinda, T. Wongsatho, S. Wisadsud, and A Chaiwongyen, "Recommendation system with limited time for visiting museum," International Conference on Culture Technology, Pattaya, Thailand, pp. 199-204, 2019.
  13. A. Rajaraman, J. D. Ullman, "Data mining" (PDF), Mining of Massive Datasets, pp. 1-17, DOI: 10.1017/CBO9781139058452.002.
  14. T. Wongsatho, L. Kovavisaruch, T. Sanpechuda, K. Chinda, S. Wisadsud, and A. Chaiwongyen, "The development of museums network guide system by using QR code on smart phone," WMS Journal of Management, vol. 4, no. 1, pp. 12-22, 2016.