Using collaborative models to adaptively predict visitor locations in museums

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

8 Citations (Scopus)

Abstract

The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor’s experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor’s activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor’s next locations in a museum, and an ensemble model that combines their predictions. Our experimental results from a study using a small dataset of museum visits are encouraging, with the ensemble model yielding the best performance overall.
Original languageEnglish
Title of host publicationAdaptive Hypermedia and Adaptive Web-Based Systems
Subtitle of host publication5th International Conference, AH 2008. Proceedings
EditorsWolfgang Nejdl, Judy Kay, Pearl Pu, Eelco Herder
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages42-51
Number of pages10
ISBN (Electronic)9783540709879
ISBN (Print)9783540709848
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2008 - Hannover, Germany
Duration: 29 Jul 20081 Sep 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5149
ISSN (Print)0302-9743

Conference

Conference5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2008
CountryGermany
CityHannover
Period29/07/081/09/08

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