Title
Adaptive, intelligent presentation of information for the museum visitor in PEACH
Abstract
The study of intelligent user interfaces and user modeling and adaptation is well suited for augmenting educational visits to museums. We have defined a novel integrated framework for museum visits and claim that such a framework is essential in such a vast domain that inherently implies complex interactivity. We found that it requires a significant investment in software and hardware infrastructure, design and implementation of intelligent interfaces, and a systematic and iterative evaluation of the design and functionality of user interfaces, involving actual visitors at every stage. We defined and built a suite of interactive and user-adaptive technologies for museum visitors, which was then evaluated at the Buonconsiglio Castle in Trento, Italy: (1) animated agents that help motivate visitors and focus their attention when necessary, (2) automatically generated, adaptive video documentaries on mobile devices, and (3) automatically generated post-visit summaries that reflect the individual interests of visitors as determined by their behavior and choices during their visit. These components are supported by underlying user modeling and inference mechanisms that allow for adaptivity and personalization. Novel software infrastructure allows for agent connectivity and fusion of multiple positioning data streams in the museum space. We conducted several experiments, focusing on various aspects of PEACH. In one, conducted with 110 visitors, we found evidence that even older users are comfortable interacting with a major component of the system.
Year
DOI
Venue
2007
10.1007/s11257-007-9029-6
User Model. User-Adapt. Interact.
Keywords
Field
DocType
Adaptive mobile guides,Multimodal user interfaces,Personalized information presentation,Personal visit report
Interactivity,World Wide Web,Suite,Computer science,Mobile device,Human–computer interaction,User modeling,User interface design,User interface,Visitor pattern,Personalization
Journal
Volume
Issue
ISSN
17
3
0924-1868
Citations 
PageRank 
References 
94
3.60
41
Authors
9
Name
Order
Citations
PageRank
Oliviero Stock11159152.23
Massimo Zancanaro21160108.89
Paolo Busetta337130.50
Charles Callaway41256.12
Antonio Krüger51537127.04
Michael Kruppa6943.60
Tsvi Kuflik71280111.75
Elena Not842839.78
Cesare Rocchi917011.53