A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture

Abstract

Cultural Heritage Institutions (CHI) are increasingly aiming at enhancing their visitors' experiences in a personalised, immersive and engaging way. A personalised system for cultural heritage promotion potentially adapts, in terms of relevance, content and presentation according to the user’s interests and needs. However, since a typical visit may be short and unrepeatable, the identification of user’s profile must be quick and efficient, ensuring the successful respective personalisation process.
Current paper discusses a methodology for cultural user personas extraction and identification. The CURE approach eliminates the requirement of explicit user input via registration or similar data acquisition methods and involves three main stages: data acquisition from the user’s online and social activity, reasoning regarding persona similarity and finally data and experiences reuse from previous visits. Regarding the constructed personas, the proposed approach continuously adapts and refines the personas features from data gathered during multiple cultural experiences and accordingly creates, deletes or merges personas in case of significant deviation, poor correlation and convergence respectively.

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Published by BCS Learning and Development Ltd.
Proceedings of British HCI 2018 Conference, Belfast, Northern Ireland
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A Methodology for Optimised Cultural User
peRsonas Experience - CURE Architecture
Cultural Heritage Institutions (CHI) are increasingly aiming at enhancing their visitors' experiences
in a personalised, immersive and engaging way. A personalised system for cultural heritage
promotion potentially adapts, in terms of relevance, content and presentation according to the
user’s interests and needs. However, since a typical visit may be short and unrepeatable, the
identification of user’s profile must be quick and efficient, ensuring the successful respective
personalisation process.
Current paper discusses a methodology for cultural user personas extraction and identification.
The CURE approach eliminates the requirement of explicit user input via registration or similar
data acquisition methods and involves three main stages: data acquisition from the user’s online
and social activity, reasoning regarding persona similarity and finally data and experiences reuse
from previous visits. Regarding the constructed personas, the proposed approach continuously
adapts and refines the personas features from data gathered during multiple cultural experiences
and accordingly creates, deletes or merges personas in case of significant deviation, poor
correlation and convergence respectively.
User Personas, Personalisation, Cultural mobile applications, Evaluation, Cultural User Experience
1. INTRODUCTION
Peter having read some favourable posts on social
media, decides to visit a new exhibition in one of
the museums of the city. Standing on the entry of
the museum, he carries expectations of an
interesting cultural visit, yet is unsure on how to
start. Peter asks the curators whose first advice is
to connect through his mobile device to the
museum’s Facebook page in order to download the
application, which offers a personalized path. As
Peter follows the suggested route into the physical
museum space, a path through the mind is walked
as well, discovering personalised experiences.
Recently, interactive experiences in Cultural
Heritage Institutions (CHI) are produced in an
increased pace. One of the main purpose of this
interest has been to enhance user’s interaction with
cultural objects by adjusting the cultural experience
to his expectations and needs (Hassenzahl, 2008;
Vermeeren et al., 2010). The importance of high
quality User eXperience (UX) is increasing as
user’s base of current trends in computing is
constantly growing and shifting. Thus, the
evaluation of the visitors’ experiences using new
technologies (Konstantakis et al., 2017) (such as
Ubiquitous computing, Internet of Things, Context
Αwareness) is of importance for CHI, but as yet
unexplored (Chianese & Piccialli, 2015).
Since digitisation is changing the cultural
landscape, CHI have been struggling with their
exhibits and their environments. Previously, the
cultural space has acted as a site of preservation,
storage and exhibition, as well as information
dissemination. Currently, they have changed to
take on more concrete functions such as enabling
and facilitating active learning and visitors’
engagement with exhibits, as well as active
collaboration on information seeking and sharing
between visitors (Othman, 2012). The convergence
of the web has made the exploration of cultural
heritage a continuous process, starting before the
visit and ideally never ending, as the user is able to
plan the visit online, visit the site, and then “revisit”
places of interest online again.
In this context, applying a methodology which will
engage the user into the provision of profiling
information might in fact decide the success or
failure of the cultural experience. The user profile
initialisation is also an important aspect for
personalised cultural applications and a particularly
challenging task to tackle. Thus, in the field of
cultural heritage there have also been efforts to
minimise the problems of personalisation
initialisation by using ‘personas’ (Kenteris, Gavalas
& Economou, 2011).
In this paper, we propose the CURE methodology
(Cultural User peRsonas Experience) for
enhancing user personas experiences in CHI, by
incorporating data acquisition techniques from
social activity or previous experiences, rather than
explicit user input. In the rest of the paper we
present the related work and studies and we
analyze the CURE methodology for the

A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture
Konstantakis ● Michalakis ● Aliprantis ● Kalatha ● Moraitou ● Caridakis
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identification of user personas. Finally, we describe
the backend architecture of the CURE proposed
methodology and afterwards we discuss our future
plans and directions.
2. RELATED WORK
The technologies used in some studies (Cheverst
et al., 2000; Vassilakis et al., 2016) were tailored to
suit the needs of particular cultural spaces
providing a wide range of technological means to
citizens and venue visitors in a highly personalised
manner. Nevertheless, users found that is harder to
use a system with too many options available.
Some researchers have analysed the visitor’s
personalised experience from a philosophical
perspective (Ardissono, Kuflik & Petrelli, 2012;
Findlater & McGrenere, 2004) addressing the
challenges of socialisation and collaboration in
small and large-size communities, whilst others
have addressed different aspects of the visitor
experience empirically, including the cognitive,
intellectual and emotional aspects (Antoniou &
Lepouras, 2010).
In addition, others have investigated the interaction
between individual visitors and exhibits, or between
visitors in groups and exhibits, collecting
information prior to the visit (Falk, 2006; Kuflik et
al., 2011).
Furthermore, in the field of cultural heritage there
have also been efforts to minimise the problems of
personalisation initialisation with the use of
‘personas’ (Bonis et al., 2009; Roussou et al.,
2013) and to improve the usability of mobile
applications through context awareness, resulting
in a better UX (Davies, 2007). Still there are issues
such as the exact number and defined variables of
the selected personas. Moreover, in some cases
the user does not correspond to any of the
personas or match equally with more than one, a
significant problem which need to be addressed.
3. PERSONALISATION
Personalisation (Antoniou & Lepouras, 2010) is
based on the assumption that an application can
understand the user’s needs, while its success
relies greatly on the accurate elicitation of the user
profile. In a typical cultural space visit the users’
time is limited. It may last as little as a few minutes,
and the users might only visit the cultural site once.
An increasing number of CHI around the world use
personalised, mostly mobile, guides to enhance
visitors’ experiences, attract new visitors and
address the needs of a diverse audience. The use
of personalisation technologies has now become
very common in CHI. However, there is still a lack
of understanding about how visitors interact
simultaneously both with such methods and the
exhibits.
The diversity of visitors to CHI is one of their unique
attributes and therefore is becoming a major
challenge for these venues to meet their visitors’
needs. How can they address the variety of
interests and needs of all their visitors?
According to (Falk, 2006; Morris, Hargreaves &
McIntyre, 2004) there are four different modes of
behaviour among visitors in CHI, especially when
they select and engage with the exhibits:
‘browsers’, ‘followers’, ‘searchers’ and
‘researchers’. These four types of visitor may prefer
different kinds of information presentation and
therefore different technologies can be
implemented/used.
Browsers, for example, do not require as much
information as researchers because they only
browse and select exhibits that most appeal to
them. On the other hand, Researchers require
more explanation about each artefact in the
exhibition and may require extra information related
to the exhibits.
Followers, in contrast, only follow what has been
provided to them and usually will be happy with the
use of the mobile guide provided by the CHI. A
Searcher is quite different from the other groups
because he prefers to search the exhibit based on
keyword(s) rather than the thematic presentation.
Figure 1: The 4 different types of visitors
Additionally, the authors in another research
(Walsh, Clough & Foster, 2016) identify the ways in
which users of Digital Cultural Heritage (DCH)
systems and services have been categorised. They
proposed that it may be better to categorise users
by expertise, than by label or user type.

A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture
Konstantakis ● Michalakis ● Aliprantis ● Kalatha ● Moraitou ● Caridakis
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Alternatively, it could be applied some combination
of both, since it would potentially enable the
delivery and creation of more value-added
services.
4. CURE METHODOLOGY
4.1 The cold start problem – User Personas
approach
Although personalisation is useful in cultural
heritage, creating correct visitor profiles is a rather
demanding and sometimes intruding task basically
due to the short duration of most visits and the fact
that most visitors might only visit a specific
institution only once (Vassilakis et al., 2016).
Within these time restrictions, visitor profiles need
to be created quickly and effectively in terms of
their appropriateness for the different visitors. The
problem is that although visitors enjoy the benefits
of personalisation in cultural heritage, they are at
the same time reluctant at dealing with form-filling
activities. Therefore, researchers have to become
more creative in applying indirect approaches for
the collection of the needed information for the
creation of user profiles.
The design of personas as ‘fictional’ characters is
considered as a very consistent and representative
way to define actual users and their goals.
However, it is important to clarify the exact
(minimum) number of personas in each occasion in
order to focus on the visitor profiles to be
examined.
Moreover, in many cases these profiles are
designed and built based on user input (surveys
and questionnaires, records of user’s patterns and
moves), and as a result they represent only the
average visitor profiles. Additionally, input data is
not always in correspondence with the actual
desires and interests of the user. What if the visitor
doesn’t really know in what he is most interested or
visits a museum for the first time?
To be more accurate on the design of user
personas, there is the need to rely on different
sources for users’ information, without distracting
them from their cultural experience. Hence, during
the start of a system, i.e. when a user is new to that
system, the system fails to recommend paths and
directions initially. This problem is called the cold
start problem in personalisation.
Cold start problem is a well-known problem in
recommender systems. Many solutions and
methods are proposed to address this issue.
Common recommendation strategies are based on
association rules and clustering techniques
(Sobhanam, 2013), social information (Zhang et al.,
2010; Noor & Martinez, 2009), ontological
knowledge classification (Noor & Martinez, 2009)
and hybrid user modelling approach (Wang et al.,
2008).
The proposed methodology will adopt Noor’s and
Martinez’s ontological knowledge of social
information and takes into consideration alternative
sources of input from the user’s digital and social
activity. Applying reasoning techniques regarding
persona matching, CURE performs an accurate
identification procedure without any previous data
concerning the user, thus partially addressing the
cold start problem.
4.2 Identification of User Persona: Methodology
steps
Personalised applications need to be able to
identify user’s personas, i.e. their set of
characteristics that defines their needs and
expectations from the application.
Although an explicit request to the user, in order to
select an appropriate persona, would seem to
work, there are some explicit disadvantages such
as the inconsistency of users’ choices and their
annoyance. Ideally, the application should be able
to identify the User Persona without any user input
or intervention and customise its functionality
accordingly. Our work aims towards that direction,
by proposing a methodology of identifying User
Personas in a cultural context.
Figure 2: The CURE Methodology

A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture
Konstantakis ● Michalakis ● Aliprantis ● Kalatha ● Moraitou ● Caridakis
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The CURE methodology involves certain stages
that are highly influenced by the data cycle of
Context Aware Computing (Perera et al., 2014),
stressing the correlation between this procedure
and other context aware functionalities that are
applied in similar situations.
These stages are: data acquisition, reasoning and
dissemination. The data flow is shown in Figure 2.
Data acquisition: The first stage corresponds to
data acquisition from multiple sources and involves
the following two modules.
- Data mining
Data mining acquires profiled data which can be
found from various repositories (mobile device,
social media, other open access web data). Data
mining techniques and natural language
recognition are required to retrieve the profiled data
of a user. Privacy and security of personal data will
need to be addressed as well in this procedure.
Typical issues with personalised applications are
mining of personal data and ethics accompanying
such procedures. Although trust from users is hard
won, it should be sought by informing the users
about the exact type of data collected and their
exact use, as well as ensuring that no sensitive
personal data will be stored.
- Ubiquitous Computing
The ubiquitous computing paradigm integrates
sensory networks and context aware procedures to
apply a personalised functionality in applications
and devices. Specifically, in CURE there are two
sources of behavioural data: (a) data retrieved from
sensors of the mobile device (during the current or
previous cultural visits) that allow a refinement of
the user persona associated with the user and (b)
data extracted from the sensory infrastructure of
the cultural space, concerning user’s activities and
behaviour establishing a more effective connection
between user and the cultural space. Due to lack of
sensed data (e.g. first visit) or the uncertainty of
interpreting behaviour, this input stream may have
the least or the most weight on the identification
algorithms. Furthermore, the ubiquitous
characteristic of transparency is beneficial in this
context, since we do not wish to burden the
cognitive load of the visitors.
Data modelling / reasoning: The second stage
includes the processing of the multiple data
sources whose wide complexity and diversity will
need to be modelled and reasoned. Sentiment
analysis will be performed on the profiled and
behavioural data, extracting usable information
about the user profile.
Eventually, the user with a customised user
persona, allowing a better understanding of his
needs and goals. The original 4 user personas
according to Morris et al., (2004) will be used as a
basis which will be enhanced according to specific
characteristics of each cultural user persona.
Data dissemination: The specialised persona of
the user will be disseminated to the cultural
application, allowing a customizable presentation of
the various activities and attractions of the cultural
place. Furthermore, as user explores the cultural
application, the latter will continue to observe and
collect behavioural data, sending feedback to the
data modelling and reasoning components,
allowing for further refinement of user personas
and more efficient customisation of the cultural
experience.
This cycle of persona identification will be
continuously performed during the whole cultural
visit of the user, eventually storing the identified
persona for future use. Furthermore, the
infrastructure of the cultural space also acquires
the identified user persona and accordingly
customises the user experience. Consequently, the
whole cycle runs in a ubiquitous ecosystem where
user, digital tools for artefacts and other
infrastructure adapt to each other.
The architecture will also integrate a method for
evaluation of the persona identification procedure.
A specialised component is responsible for the
collection of manual data concerning the user that
will be checked against the output of the
identification methodology. The differences
between correct (manual) and identified personas
will allow for an effective tuning of the algorithm so
that it performs in a high level of accuracy.
5. THE CURE ARCHITECTURE
The cultural heritage experience is being viewed as
an ongoing lifelong experience: curators and
cultural researchers are continuously looking at
how visitors can be captured and retained over
time, both online and onsite.
Users’ personalisation can play a major role by
reasoning on past experience and other daily and
contextual characteristics, making the current
cultural heritage experience a link in a lifelong
chain.
This creates a series of challenges that accompany
lifelong experience user modelling in general:
collecting data, remembering and forgetting (as the
user’s characteristics change), privacy and user
control. This lifelong experience through the user
personas identification is the basis of our CURE
methodology. The CURE components will be built
to form the back-end environment (Figure 3).

A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture
Konstantakis ● Michalakis ● Aliprantis ● Kalatha ● Moraitou ● Caridakis
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Figure 3: The CURE Architecture
5.1 The CURE back-end
The CURE back-end hosts the information
repository, as well as all services needed to
support the visitor's application runtime. Also, it
encompasses a repository for the storage,
organisation and retrieval of cultural information
and metadata, while it additionally hosts the CURE
modules for delivering the needed functionalities to
the application in the CHI.
To accommodate the stages listed in the previous
architecture, the CURE back-end functional
modules comprising the system are the following 6
modules:
Data acquisition component: It handles data
acquisition and modelling, sending ontologies and
modelled data to the user persona identification
module. The data are either mined from the web
and social network concerning the user’s cultural
preference, or collected from the physical
infrastructure of the CHI (sensors measuring
environmental or user variables).
Administration Module: This module can be used
exclusively by the system administrator performing
important operations for system management.
DB (Database) Module: This module is
responsible to store and manage all the produced
information that is relevant to the users. It
communicates with all the modules while having no
connectivity with outside elements for security and
sustainability reasons.
User Persona Identification module: In this
module, the main algorithms of data reasoning are
executed, inferring the parameters of the persona
of the current user. The input is retrieved from the
Data Acquisition component and the output is
stored to the Database module for further use. The
module constantly updates the persona as more
behavioural data arrive.
Interaction module: This module is responsible to
retrieve interaction data. During his visit, the user
will interact with elements of the cultural
application, whose actions will feed the back-end
with more data on his preferences and will allow
further customisation of the UX.
Data dissemination module: This module handles
all the necessary communication with the cultural
application and the infrastructure of the institution
about the customisation actions needed to enhance
the user experience of a specific user or group of
users. According to the information stored in the
DB module, certain actuation calls are sent to the
actors involved in providing the personalised
cultural user experience (e.g. the screen of an
artefact near the user).
6. USE CASE
Let us return to Peter who is visiting another
museum that integrates the CURE architecture.
Peter has been identified by CURE as a “Follower”,
based on his previous visits to cultural spaces. His
profile incorporates the basic characteristics of the
persona (i.e. following suggested paths) while also
including personal traits such as his preference for
modern art exhibits and paintings. Also, his
average visit time is higher than the general
average time of the visitors.
Upon entry at the museum, Peter is automatically
registered to CURE platform. The museum’s
Application (App) requests Peter’s profile, initiating
the personalisation procedure. Furthermore, CURE
requests access to his latest social media posts, to
apply sentiment analysis and possibly extract
opinions that can be used in this context. Indeed,
his twitter comment “Today is cultural day! I really
want to check Beja’s new artefact” indicates which
exhibit will be at the center of Peter’s attention. This
information is also sent to the App.
Taking into consideration the profiled data
disseminated from CURE, the App designs a
personalised route for Peter. Since he is a follower,
Peter is likely to accept the App’s proposed route
and start his visit by following the path, which is
adapted to (a) his average visit time, (b) his cultural
preferences and (c) any opinions mined from social

A Methodology for Optimised Cultural User peRsonas Experience - CURE Architecture
Konstantakis ● Michalakis ● Aliprantis ● Kalatha ● Moraitou ● Caridakis
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posts. The App is not responsible for gathering
personalised data but instead to customise
predefined routes and procedures according to the
persona characteristics identified by CURE.
As a “Follower”, Peter is expected to follow the path
suggested by the App. Yet, during this visit, Peter
shows independency above a certain threshold,
which adds some “searcher” traits to his persona.
More specifically, he tends to ignore certain types
of exhibits, while skipping sub paths and diverting
his route to other locations. This information is
extracted from Peter’s mobile location sensors and
from the museum’s sensory infrastructure.
CURE (executed at the background) extracts the
behavioural data and repeats the personalisation
procedure, enhancing the original persona with
Peter’s specific preferences of the current cultural
space and its exhibits. The modified and updated
persona is disseminated to the App, which
redesigns the paths and the functions provided to
Peter. The interface of the App is now split into two
sections (a) the suggested path and (b) selected
artefacts which Peter can select and be directed
towards them.
Peter’s visit continues and finally ends without any
other major reiterations of the personalisation
procedure. Finally, considering Peter’s previous
visits which indicate an inclination to buy
memorabilia from the museum’s shop, the App
suggests a visit at the shop, as he is exiting the
museum.
7. DISCUSSION AND FUTURE WORK
In this research, we have extended existing work
(Cheverst et al., 2000; Davies, 2007) by designing
a new user personalisation method based on user
personas, the CURE methodology.
Its contribution focuses on the design of user
personas not based on explicit input data, but on
data acquisition from the user’s online and social
activity, thus eliminating inconsistency of user’s
choices or distraction of cultural experience.
Furthermore, CURE methodology incorporates
context aware techniques and behavioural data
from the current or previous visits to cultural spaces
to refine the personalization process.
Although promising, the fusion of many input
streams at the personalization procedure may not
deliver an accurate persona identification. Natural
language processing and sentiment analysis
technologies have not yet reached their maximum
potential, while contextual data acquisition and
reasoning may lack the precision needed in some
cases. As a result, it is crucial to integrate a proof
of concept component which will request user
feedback and process it by adapting accordingly
the persona. Overall, we are confident that the
evaluation of the CURE methodology against more
traditional static cultural applications will prove its
merits and advantages towards a better CUX.
Also, it is worth noting that the suggested CURE
framework has additional important benefits for the
museum or cultural space. The satisfaction of high
quality CUX offered by personalized suggestions
through CURE methodology will potentially
stimulate the visitor to come back and reuse the
system or to encourage other people to try it as
well. This can be economically advantageous for
museums, which can expect an increase in virtual
and real visitors as a result of CURE
personalization. So, by changing the museum
orientation to be more visitor-centered, based on
visitors’ needs, museums can be viewed as an
essential cultural service.
Based on these assumptions, our future work
involves the integration of CURE methodology into
a cultural application. This will allow us to evaluate
the effect of User Personas in Cultural User
Experience and whether the suggested
methodology does succeed in offering a framework
for personalized applications, in the scope of
cultural visits.
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