=Paper=
{{Paper
|id=Vol-2176/paper2
|storemode=property
|title=Personalisation of digital museum guides through implicit recognition of visitor personas
|pdfUrl=https://ceur-ws.org/Vol-2176/paper2.pdf
|volume=Vol-2176
|authors=Moneerah Almeshari,John Dowell,Julianne Nyhan
|dblpUrl=https://dblp.org/rec/conf/mhci/AlmeshariDN18
}}
==Personalisation of digital museum guides through implicit recognition of visitor personas==
https://ceur-ws.org/Vol-2176/paper2.pdf
Personalisation of digital museum guides through implicit
recognition of visitor personas
Moneerah Almeshari, John Dowell, Julianne Nyhan
University College London
London, United Kingdom
{moneerah.almeshari.17, j.dowell, j.nyhan}@ucl.ac.uk
Abstract. Digital museum guides promise a transformed visitor experience
through greater engagement with the museum content and activities [1]. Realis-
ing that promise turns on the personalisation of digital guides, particularly in
museum contexts where rich content is accessed by a highly diverse visitor
population [2]. Since users of a museum guide are typically first time users and
since their usage is for a relatively short session, personalisation must use initial
interaction data to associate the user with a particular persona and thereby infer
other facts about the user’s preferences and needs. Two research aims follow:
first to better understand the requirements of different visitor personas, and sec-
ond, to develop methods for unobtrusively detecting a user’s persona from their
interactions with a guide and their activity in the museum space. This paper
presents the design of a research programme for: first, investigating mecha-
nisms for automatic adaptation of digital museum guides based on identifying a
visitor’s persona category from interaction data; second, exploring the require-
ments of different visitor categories to derive the user interface adaptation, and;
third, investigating the effectiveness of this adaptation on the museum visitor’s
experience.
Keywords: Adaptive mobile guide, Automatic user modeling
1 Introduction
Personalisation in individual access to Cultural Heritage has been an active topic of
research and practice for more than 20 years [2]. The importance of personalizing
guidance and support to museum visitors during their visit has been acknowledged by
many studies and the effect of differences between visitors on their experience of the
setting is well recognised [3]. Since digital mobile guides are able to provide a rich
array of facilities to users such as, information about objects, recommendations, navi-
gation and tours services, it is important that they are provided adaptively to enhance
the visitor’s experience rather than being a source of distraction [1].
This position paper outlines a proposal for a study to investigate mechanisms for
enabling automatic adaptation of museum mobile guides based on visitor category
detected implicitly from the user’s activity. Examples of users’ activity when interact-
ing with the mobile guide include: the first menu choice; the sequence of choices;
Copyright held by the author(s).
2
gestures, and; information viewing behaviour. This can be supplemented with features
of behaviours inside the museum, such as: position; time spent in front of objects,
and; exploration style. The study exploits an established taxonomy of museum visi-
tors (i.e., personas) that distinguishes visitors based on their visit motivation as: Ex-
plorers; Facilitators; Experience Seekers; Professional/Hobbyists, or; Rechargers [3].
In addition, the study explores the needs and preferences of different visitor persona
categories as the basis for variant UI adaptation where the focus will be on interaction
style and information presentation adaptation. Finally, it investigates the effectiveness
of this adaptation on museum visitors’ experience.
2 Related Work
Adaptive Mobile Guides (AMG) are mobile apps that have the capability to tailor
their interface elements, services, content and recommendations for individual users
based on the context and/or user profile [1]. AMGs seek to provide the right service
or content presented in the most suitable way in the right place at the right time to the
right person [1]. Employing AMGs to support visitors in exploring museums is one of
the active topics in the Cultural Heritage field. Several projects, including CULTURA
[4] and CHIP [5], have studied the provision of personalized services to museum
visitors using mobile guides in museums.
A user model is the basis of personalisation and consequently, the quality of the
user model directly affects the quality of the personalisation [6]. User models can be
constructed implicitly or explicitly [6]. Although building a user model based on ex-
plicit information can be more reliable and less challenging, studies have found that
users prefer tailoring the interface without being asked a series of preliminary ques-
tions [7]. HyperAudio [8], Hippie [9], PEACH [10] and CHIP [5] are examples of
projects that adapt mobile guides in museums based on values detected implicitly
during the user’s visit.
HyperAudio uses the museum visitor route and position to infer his/her interest and
knowledge [8]. Hippie models user knowledge based on the user interaction with the
interface when presenting content [9]. Positive evidence of knowledge is detected
when the user views the whole content whereas, not viewing or skipping content is
interpreted as evidence of a lack knowledge [9]. Hippie uses the user model to predict
the information need of the visitor at different points in the museum and to make rec-
ommendations [9].
Hippie models user interests based on the user’s navigation of both the physical
and informational spaces [9]. For example, detecting that the user has stopped in front
of an object for a certain time and his/her behaviour in viewing information about that
object using the mobile guide compromise two predictors that can infer about the user
interest [9]. Level of interest is inferred from the time spent in front of an object and
from the viewings of information pieces about the object provided by the mobile
guide [9]. CHIP adapts recommendations and tours to individual users based on their
interests [5]. However, it requires the user to create their tour in order to initialize the
user model and then update the user model automatically based on the user behaviour
3
inside the museum [5]. PEACH [10] models a user’s interest from their physical be-
haviour inside the museum.
Categorizing museum visitors and understanding their needs assists in the devel-
opment and the personalisation of information systems [11]. Different studies have
been conducted to categorize museum visitors based on different aspects such as,
expertise, age, motivation and exploration style [11]. Attempts to detect a visitor’s
category implicitly by sensing their behaviour inside the museum have been reported,
such as, [12] which studied detecting a visitor’s exploration style, and [13] which
differentiated between categories of pairs of visitors based on their behaviour.
3 Research Proposal
The next challenge in this field, we believe, is the development of museum mobile
guides that spontaneously adapt by detecting a visitor’s persona category implicitly
from their interactions both with the museum and the guide itself. We propose a re-
search programme to address this challenge.
A questionnaire has been developed for use as an instrument to categorize museum
visitors into one of the five categories noted earlier [3]. Five factors have been ex-
tracted from [3] to differentiate between different personas, viz: visit motivation; visit
success criteria; social aspects; number of visits, and; knowledge of museum content.
The first stage of the programme is to validate this questionnaire.
The requirements of different groups will be investigated to lead the mobile guide
adaptation. Visitors’ interactions with the mobile guide and their behaviour while
exploring the museum will be observed and captured to identify patterns from their
activities which can be used for automatic detection of the visitor persona. For exam-
ple, the visitor exploration style and their mobile guide menu choices might be a
source that can be used to differentiate between visitor personas, model the user and
subsequently lead the mobile guide adaptation. After identifying these patterns,
mechanisms that can detect these behaviours automatically will be developed.
Evaluation of the investigated mechanisms and the effect of mobile guide adapta-
tion on the visitor experience would be conducted at the last stage of the study. Adap-
tations might include providing more detailed information to some visitors and more
explanations of basic information to other visitors. The Mobile Multimedia Guide
Scale (MMGS) and Museum Experience Scale (MES) developed by [14] will be used
to evaluate the effectiveness of the adaptation on the visitor experience.
This research aims to investigate the following questions:
Q1. How can user modelling methods distinguish between the personas of users of
walk-up-and-use mobile guide systems?
Q2.1. Can different user personas be detected automatically using interaction
logs and/or users behaviour inside a museum?
4
Q2.2. Do individual visitors have characteristic behavioural pattern when
exploring the museum and using the guide? If so, what are they and how can
they be captured and used to adapt a mobile guide?
Q3. How should the user interface of a mobile guide adapt to accommodate the needs
of different visitors in the museum?
Q4. How does this adaptation enhance the visitor’s experience inside the museum?
To address these questions, the following set of objectives has been set in Table 1.
Table 1. Research Objectives.
Objective Method:
1 Differentiate between museum 1, Questionnaire that can work as a tool to clas-
visitors based on their motiva- sify museum visitors within the five categories
tion of the museum visit. identified by Falk [3].
2 Identify the requirements and 1, Design prototype of an adaptive mobile
needs of the five visitor clas- guide. 2, Questionnaire survey. 3, Short inter-
ses. views with museum visitors. 4, Observation.
3 Evaluate the correlation be- 1, Develop a digital interactive mobile guide
tween visitor categories and with different features intended for different
their preferences. persona. 2, Questionnaire from the previous
study to identify the visitor persona. 3, Obser-
vation. 4, Interviews with museum visitors. 5,
A questionnaire that enables the visitor to rank
different guide features.
4 Investigate how to detect visi- 1, Field study that analyses users’ behaviour of
tor category implicitly. different categories inside the museum. 2,
Analysis of visitors interactions with the guide.
5 Evaluate the relation between 1, Apply mechanisms that can collect infor-
implicit information and visi- mation of the defined parameters to identify the
tors categories found from the visitor category,
previous study and investigate 2, Field study where the proposed mechanism
mechanisms that can be used can be tested.
to detect visitors categories
automatically.
6 Evaluate the effectiveness of 1, Questionnaire,
the adaptation in improving 2, Short interviews.
visitor experience.
4 Conclusion
In conclusion, three contributions are expected of the proposed research. First, identi-
fication of interactions and behaviour patterns that can be used to differentiate be-
tween different museum visitor groups. Second, identification of mechanisms and
parameters that can be used to detect the visitor persona group implicitly. Third, re-
quirements of different groups and the associated adaptation features.
5
References
1. Emmanouilidis, C., R.-A. Koutsiamanis, and A. Tasidou, Mobile guides:
Taxonomy of architectures, context awareness, technologies and
applications. Journal of network and computer applications, 2013. 36(1): p.
103-125.
2. Ardissono, L., T. Kuflik, and D. Petrelli, Personalization in cultural
heritage: the road travelled and the one ahead. User modeling and user-
adapted interaction, 2012. 22(1-2): p. 73-99.
3. Falk, J.H., Identity and the museum visitor experience. 2016: Routledge.
4. Hampson, C., et al., The CULTURA Project: Supporting Next Generation
Interaction with Digital Cultural Heritage Collections, in Progress in
Cultural Heritage Preservation: 4th International Conference, EuroMed
2012, Limassol, Cyprus, October 29 – November 3, 2012. Proceedings, M.
Ioannides, et al., Editors. 2012, Springer Berlin Heidelberg: Berlin,
Heidelberg. p. 668-675.
5. Roes, I., et al. A personalized walk through the museum: The chip interactive
tour guide. in CHI'09 Extended Abstracts on Human Factors in Computing
Systems. 2009. ACM.
6. Frias-Martinez, E., et al., Automated user modeling for personalized digital
libraries. International Journal of Information Management, 2006. 26(3): p.
234-248.
7. Gauch, S., et al., User profiles for personalized information access, in The
adaptive web. 2007, Springer. p. 54-89.
8. Petrelli, D. and E. Not, User-centred design of flexible hypermedia for a
mobile guide: Reflections on the hyperaudio experience. User Modeling and
User-Adapted Interaction, 2006. 16(1): p. 85-86.
9. Oppermann, R. and M. Specht. A context-sensitive nomadic exhibition guide.
in International Symposium on Handheld and Ubiquitous Computing. 2000.
Springer.
10. Stock, O., et al., Adaptive, intelligent presentation of information for the
museum visitor in PEACH. User Modeling and User-Adapted Interaction,
2007. 17(3): p. 257-304.
11. Walsh, D., P. Clough, and J. Foster, User Categories for Digital Cultural
Heritage. 2016.
12. Zancanaro, M., et al. Analyzing museum visitors’ behavior patterns. in
International Conference on User Modeling. 2007. Springer.
13. Dim, E. and T. Kuflik, Automatic detection of social behavior of museum
visitor pairs. ACM Transactions on Interactive Intelligent Systems (TiiS),
2015. 4(4): p. 17.
14. Othman, M.K., Measuring visitors' experiences with mobile guide
technology in cultural spaces. 2012, University of York.