Abstract
In this paper we propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects. The main idea is to measure the interests of a spectator w.r.t. an artwork by means of a model able to describe the behaviour dynamics. In this approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. The key idea of this paper consists in comparing a strengthened validation approach for neural networks based on classification with our novel proposal based on clustering; indeed, clustering allows to discover natural groups in the data, which are used to verify the neuronal response and to tune the computational model.
Preliminary experimental results, based on a phantom database and obtained from a real world scenario, are shown. They underline the accuracy improvements achieved by the clustering-based approach in supporting the tuning of the model parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A multimedia recommender system. ACM Trans. Internet Technol. 13(1), 3:1–3:32 (2013)
Amato, F., Chianese, A., Mazzeo, A., Moscato, V., Picariello, A., Piccialli, F.: The talking museum project. Procedia Comput. Sci. 21, 114–121 (2013)
Bianchi, D., De Michele, P., Marchetti, C., Tirozzi, B., Cuomo, S., Marie, H., Migliore, M.: Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit. HIPPOCAMPUS 24(2), 165–177 (2014)
Chianese, A., Marulli, F., Moscato, V., Piccialli, F.: SmARTweet: a location-based smart application for exhibits and museums. In: Proceedings - 2013 International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013, pp. 408–415 (2013)
Chianese, A., Marulli, F., Piccialli, F., Valente, I.: A novel challenge into multimedia cultural heritage: an integrated approach to support cultural information enrichment. In: Proceedings - 2013 International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2013, pp. 217–224 (2013)
Cuomo, S., De Michele, P., Chinnici, M.: Parallel tools and techniques for biological cells modelling. Buletinul Institutului Politehnic DIN IASI, Automatic Control and Computer Science Section, pp. 61–75. LXI (2011)
Cuomo, S., De Michele, P., Piccialli, F.: A performance evaluation of a parallel biological network microcircuit in neuron. Int. J. Distrib. Parallel Syst. 4(1), 15–31 (2013)
Cuomo, S., De Michele, P., Posteraro, M.: A biologically inspired model for describing the user behaviors in a cultural heritage environment. In: SEBD 2014, 22nd Italian Symposium on Advanced Database Systems, Sorrento Coast, 16th–18th June 2014
Cuomo, S., De Michele, P., Ponti, G., Posteraro, M.: A clustering-based approach for a finest biological model generation describing visitor behaviours in a cultural heritage scenario. In: DATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications, pp. 427–433 (2014)
DATABENC, High Technology District for Cultural Heritage. http://www.databenc.it
Domingos, P.: A few useful things to know about machine learning. Commun. ACM. 55(10), 78–87 (2012)
Hartigan, J.A.: Clustering Algorithms. Applied Statistics. Wiley, New York (1975)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River (1988)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum. 37(2), 18–28 (2003)
Kleinberg, J.: The convergence of social and technological networks. Commun. ACM 51(11), 66–72 (2008)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Yu, P.S., Han, J., Faloutsos, C. (eds.) Link Mining: Models, Algorithms, and Applications, pp. 337–357. Springer, New York (2010). http://dx.doi.org/10.1007/978-1-4419-6515-8_13
Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Capturing interest through inference and visualization: ontological user profiling in recommender systems. In: Proceedings of the 2nd International Conference on Knowledge Capture, pp. 62–69 (2003). ISBN: 1–58113-583-1
Pentland, A.S.: Automatic mapping and modeling of human networks. Physica A. 378(1), 59–67 (2007)
Roderick, J.A., Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley Editor, New York (2002). ISBN: 978-0-471-18386-0
Weka, Data Mining Software in Java. http://www.cs.waikato.ac.nz/ml/weka/
Widyantoro, D.H., Ioerger, T.R., Yen, J.: Learning user interest dynamics with a three-descriptor representation. J. Am. Soc. Inf. Sci. Technol. 52(3), 212–225 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cuomo, S., De Michele, P., Ponti, G., Posteraro, M.R. (2015). Validation Approaches for a Biological Model Generation Describing Visitor Behaviours in a Cultural Heritage Scenario. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds) Data Management Technologies and Applications. DATA 2014. Communications in Computer and Information Science, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-319-25936-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-25936-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25935-2
Online ISBN: 978-3-319-25936-9
eBook Packages: Computer ScienceComputer Science (R0)