Information Technology seminars

Using Adaptive Collaborative User Models to Predict Visitor Locations in Museums

Date and time:

16/07/2008, 14:00

Location:

Building: 26, Room: 135, Clayton Campus

Presenters:

Mr Fabian Bohnert, PhD student, Clayton School of IT

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. Mobile 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 the physical museum space. In this talk, we present two adaptive collaborative models for predicting a visitor's next locations in museum, and an ensemble model that combines their predictions. Additionally, we 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.

Speaker biographies:

Fabian Bohnert is a PhD candidate at the Clayton School of Information Technology, supervised by Ingrid Zukerman and Liz Sonenberg. The research towards his PhD 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. Fabian holds a Diplom in econo-mathematics (eqvl. Master in Econo-Mathematics) from Ulm University (Ulm, Germany), with majors actuarial studies and data mining. When undertaking his Diplom thesis and during internships in industry, his research involved probabilistic predictive data modelling and data mining.

Enquiries:

Jeanette Niehus

Research group website:

http://www.infotech.monash.edu.au/research/centres/cris/index.html