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.
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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
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