Guest Lecture: Using Collaborative Interest and Transition Models to Predict Visitor Locations in Museums
by Fabian Bohnert (Monash University, Melbourne, Australia) on 4.8.2008 at 14.15 in B222
ABSTRACT
Museums offer vast amounts of information, but a visitor's receptivity
and time are typically limited. Hence, a visitor might be confronted
with the challenge of selecting the personally interesting exhibits to
view within the available time. Electronic handheld guides offer the
opportunity to improve a visitor's experience by recommending exhibits
of interest, and personalising the content delivered for these
exhibits. The first step in this recommendation process is the
accurate prediction of a visitor's activities and interests from
non-intrusive observations of his/her behaviour. In this talk, we
present two adaptive collaborative models for predicting a visitor's
next locations in a museum, and an ensemble model that combines their
predictions. We also discuss two alternative prediction mechanisms
(set and sequence). Our experimental results from a study using a
small dataset of museum visits are encouraging, with the ensemble
model yielding the best performance overall.
SHORT BIO
Fabian Bohnert holds a Diplom degree in econo-mathematics (eqvl.
Master in Econo-Mathematics) from Ulm University (Ulm, Germany), with
majors actuarial studies and data mining. His current research towards
his Ph.D. degree at Monash University (Melbourne, Australia) is based
in the fields of user modelling and personalisation. He is
particularly interested in adaptive statistical techniques for user
modelling and recommendation in physical spaces with organised
information. When undertaking his Diplom thesis and during internships
in industry his research involved stochastic data modelling and data
mining.
WELCOME!