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models, reputation systems and classifiers able to map
human activity and social user interactions. More
in details a smart system should be have the ability
to automatically infer interests of users and track the
propagation of the information. For real life applica-
tions, in (Amato et al., 2013; Chianese et al., 2013a) a
wireless sensor network, using bluetooth technology,
able to sense the surrounding area for detecting user
devices’ presence in a museum is discussed. About
the digital user behaviours a study of the relevance
of feedbacks, typically adopted for the profiling dur-
ing long-term modeling is given in (Kelly and Tee-
van, 2003). In (Widyantoro et al., 2001) an algo-
rithm based on the descriptors representation is de-
veloped to acquire high accuracy of recognition for
long-term interests, and to adapt quickly to changing
interests in the learning user activity. Other method-
ologies using computational approaches are based on
machine-learning (Domingos, 2012). Here, the focus
is to estimate the dynamics of the users’ group mem-
bership and to characterize the social relationships by
means of behaviour patterns with statistical learning
methods. In (Pentland, 2007), using the users data
to model an individual behaviour as a stochastic pro-
cess, the authors show a framework that predicts the
future activity, obtained by modeling the interactions
between individual processes. Ontological method-
ologies for user profiling in recommender systems
are described in (Middleton et al., 2003). Finally, a
multimedia recommender system based on the social
choice problem has been recently proposed in (Al-
banese et al., 2013).
6 CONCLUSIONS
In this paper, we describe a framework that is closed
to the computational methodology, adopted to infer
information about visitors in a cultural heritage con-
text. The challenge is to map, in a realistic way, the
biological morphology of a neuron in this application
scenario. We deal with a model where the (R
m
, C
m
)
couple represents the sensitivity of the user respect to
an artwork. The main novelty of our work has been to
employ a clustering algorithm methodology to obtain
starting groups from which these electrical parame-
ters can be tuned.
A very nice issue is to adapt, in a smart way, this
computational framework to many different applica-
tion issues such as the context-aware profiling, feed-
back based system or recommendation systems. In
future research lines, we will study more complex
neuronal dynamics by morphology point of view with
the aim to develop models that are more close to the
real users. Other research tracks will be the build-
ing of computational neural networks able to repro-
duce the interactions in social cultural heritage net-
works. In addition, regarding the preliminary cluster-
ing phase, we will tune our model with more than two
clusters, with the aim of obtaining fine-grainer clus-
tering solutions that are able to capture and to high-
light other neuron aspects, apart from spike genera-
tion.
ACKNOWLEDGEMENTS
Authors thank DATABENC, a High Technology Dis-
trict for Cultural Heritage management of Regione
Campania (Italy), and ENEA Portici Research Center,
UTICT-HPC Department, for supporting the paper.
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