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 main novelty of our approach consists in resorting to clustering task to discover natural groups, which are used in the next step 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. To discuss the obtained results, we report a comparison between the cluster memberships and the spike generation; our approach resulted to perfectly model cluster assignment and spike emission.

A clustering-based approach for a finest biological model generation describing visitor behaviours in a cultural heritage scenario

Ponti, G.
2014

Abstract

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 main novelty of our approach consists in resorting to clustering task to discover natural groups, which are used in the next step 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. To discuss the obtained results, we report a comparison between the cluster memberships and the spike generation; our approach resulted to perfectly model cluster assignment and spike emission.
9789897580352
User Profiling;Clustering;Data Mining;Computational Neural Models
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12079/5059
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
social impact