SEKE 2012 Proceedings - Knowledge Systems Institute

and course. We made an experimental in order to verify
the hypotheses that the proposed architecture can bring ads
more relevant than a random approach. The hypotheses are
the following:
Null hypothesis - H 0 : The relevance of the ad (represented
by Φ) offered by this model (this) is, at most equivalent
to the random delivery(random).
H 0 :Φ this ≤ Φ random
Alternative hypothesis - The relevance of the ad (represented
by Φ) offered by this model (this) is superior
to the random
H 1 :Φ this > Φ random
During a period of seven days, the ads ware exhibited in
the coffee shop of the university. In this period the same
amount of ads ware displayed using both approaches, the
random and the one here described. The results show that
we achieve a considerable improvement in the relevance of
the ads, confirming the alternative hypothesis. More precisely,
from the total ads sent, our model achieved an accuracy
rate of 64,18%, versus 36,05% from random.
5. Conclusions
In this work we presented a novel model to represent
context in Pervasive Advertising. The main advantage of
this model is that it defines context in terms of user and
device. The big benefit of this new way of organizing information
is that it allows that new classes of information are
added to the model without causing major changes, making
the model easy to extend. In other words task ontologies
can be easily added to the model. This is possible because
the coupling occurs from the classes of information to the
source of information, leaving the classes loosely coupled
with each other.
To demonstrate the feasibility of the model, we developed
a case study on a pervasive display scenario. Through
this case study was possible to notice that the model is very
comprehensive. This can be notice because the products
advertised were from many different fields (such as sports,
computing, and beauty) and the model was used without
changes or extensions.
In this case study, was possible also to improve the effectiveness
of the advertisements in more that 77%. We notice
yet, that the results could be still more expressives if our
base of advertisements was more comprehensive and if the
bluetooh discovery was not so slow.
As current/future work, we are developing: (i) an application
for a Digital Signage scenario, that is not intrusive;
(ii) an application for an opportunistic Mobile Advertising
scenario; and (iii) a multiagent approach for Pervasive Advertising,
which contemplates the delivery of advertisement
in both public displays and the mobile personal devices.
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