Using interest and transition models to predict visitor locations in museums

Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, Liz Sonenberg

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

Abstract

Museums offer vast amounts of information, but a visitor's receptivity and time are typically limited, providing the visitor with the challenge of selecting the (subjectively) 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 adapting the delivered content. The first step in this personalisation process is the prediction of a visitor's activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that take into account the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines the predictions of these models. The three models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.
Original languageEnglish
Pages (from-to)195-202
Number of pages8
JournalAI Communications
Volume21
Issue number2-3
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Collaborative user model
  • location prediction
  • museum
  • physical space

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