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Recommending user Experiences based on extracted cultural PErsonas for mobile ApplicaTions-REPEAT methodology

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Cultural institutions today tend to favor the incorporation of mobile technologies and applications and usually offer interpretive electronic guides to their visitors, with adaptive and personalized cultural content. However, it is still unknown to what degree users approve and are satisfied with the level and depth of personalization that these technologies adopt. The purpose of this research aims to analyze the impact and quantify how personalized and obtrusive should a mobile application be and correlate these aspects with the users perception, perspective and desires. To achieve that, we propose the REPEAT methodology, that uses implicit data from users mobile device and explicit data from users answers to our REPEAT questionnaire, to categorize each visitor and assume the personalization level that fits better for his interests. Furthermore , this work will design a mobile prototype that implements the results of our research by dynamically incorporating user's feedback into the personalization process.
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Recommending user Experiences based on
extracted cultural PErsonas for mobile
ApplicaTions - REPEAT methodology
Markos Konstantakis1, John Aliprantis1, Kostas Michalakis1, and
George Caridakis1,2
1University of the Aegean, II Research Group, Mytilene, Greece
{mkonstadakis, jalip, kmichalak, gcari}@aegean.gr
http://ii.aegean.gr
2National Technical University of Athens, Intelligent Systems, Content and
Interaction laboratory, Athens, Greece
gcari@image.ntua.gr
Abstract. Cultural institutions today tend to favor the incorporation of
mobile technologies and applications and usually offer interpretive elec-
tronic guides to their visitors, with adaptive and personalized cultural
content. However, it is still unknown to what degree users approve and
are satisfied with the level and depth of personalization that these tech-
nologies adopt. The purpose of this research aims to analyze the impact
and quantify how personalized and obtrusive should a mobile applica-
tion be and correlate these aspects with the users perception, perspective
and desires. To achieve that, we propose the REPEAT methodology, that
uses implicit data from users mobile device and explicit data from users
answers to our REPEAT questionnaire, to categorize each visitor and
assume the personalization level that fits better for his interests. Fur-
thermore, this work will design a mobile prototype that implements the
results of our research by dynamically incorporating user’s feedback into
the personalization process.
Keywords: Personalization ·Cultural User Experience ·Cultural Mo-
bile Applications ·User interaction ·Methodology
1 Introduction
Nowadays, cultural institutions are consistently trying to adapt to the current
computing state of the art and integrate mobile technology and digital guides
to enhance visitors experience. As a result, galleries, libraries, archives and mu-
seums (GLAMs) are replacing traditional passive exhibitions with interactive
displays and mobile virtual tour guides to aid visitors understanding without
overwhelming their limited physical space [7, 8, 14, 16].
This tendency has effectively transformed the relationship that GLAMs main-
tain with their visitors, offering unprecedented levels of access in databases with
2 Konstantakis et al.
massive information and new opportunities for interactivity that affects substan-
tially Cultural User eXperience (CUX), considering the fact that Semantic Web
and Linked Open Data cloud databases have also been utilized to enrich cultural
information [9]. An increasing number of cultural institutions around the world
use personalized, mostly mobile, guides to enhance visitor’s experience [11] and
attract new visitors [15]. The use of personalization technologies has now become
very common in cultural institutions. However, there is still a lack of understand-
ing about how visitors interact with such methods and simultaneously with the
exhibits.
In this paper we intend to investigate the current challenges and limitations
in personalization of cultural heritage information. Moreover, we propose the
REPEAT methodology that classifies each visitor according to his interests and
actuates the desired level of personalization. REPEAT uses data from social
media and mobile sensors, while also requiring users to initially answer a small
questionnaire, to estimate their profile and adjust the digital content of the
mobile guide that they may be interested in. Therefore, REPEAT exploits the
functionalities of modern smartphone devices to offer efficient visiting services
in a friendly manner.
The rest of the paper is organized as follows: in the first section we present
the five categories that users are classified to and their characteristics according
to personalization needs. The next section presents the methodology REPEAT,
its questionnaire and architecture. Afterwards, we present the implementation of
a mobile prototype based on the REPEAT methodology and finally, we conclude
and discuss our future plans and directions.
2 Related work
Smartmuseum [10] addresses the current issues of personalization by using Se-
mantic Webs ontologies and techniques, however it is strongly related to tech-
nically prepared environments (indoors or outdoors) with the use of RFID (Ra-
dio Frequency Identification) tags or WiFi, and thus the proposed system is
depended on the exact user’s positioning. Zancanaro et al. [18] validates past
ethnographic research by applying unsupervised learning approaches to visitors
classification, underlining the constant need of categorizing and understanding
visitor’s needs in order to personalize their cultural experience . Moreover, Ardis-
sono et al. [1] advocate that personalization of cultural heritage information
enables the implementation of mobile applications that model visitors by rec-
ognizing their interests, knowledge and personal characteristics, then select the
most appropriate content, and deliver it in the most suitable way, encourag-
ing researchers and institutions to put personalization research results finally to
work in practice.
Also, Roussou et al. [13] create a set of personas representing archetypical
museum visitors, as part of a multitude of user-centered design methods used
to better understand the needs of visitors and develop for them a personalized
mobile storytelling experience and Morris et al. [12] identify four different modes
REPEAT methodology 3
of behavior among visitors in cultural institutions, especially when they select
and engage with the exhibits: browsers, followers, searchers and researchers. Dim
et al. [3] present the museum triage concept, arguing about the importance of
an early detection of museum visitors identities. The triage concept relates to an
area close to the entrance of a facility, equipped with sensors that enable massive
data collection. Finally, Karaman et al. [8] claim that each visitor’s interests and
knowledge induce a unique perspective of what is relevant among the massive
amount of available information, and propose the MNEMOSYNE system which
passively monitors user’s movements in the museum and builds its profile, which
again is strongly related to technical aspects of the environment.
3 REPEAT Methodology
3.1 Users Classification - Theoretical approach
Recent articles and studies [6, 16] state that many museums and other cultural
organizations have adopted Falk’s user classifications as a means of segmenting
online audiences, even though these classifications were devised for the physical
museum [4]. Falk believed that user classifications should not be based on demo-
graphics only and proposed an extended clustering of all the various motivations
visitors ascribe to visiting museums into just five distinct, identity-related cate-
gories. Based on their profiles and characteristics, we can assume the level and
depth of personalization, referring to the type and amount of information that
satisfies each group of visitors:
Explorers: Visitors who are curiosity-driven with a generic interest in the
content of the museum. They visit museums without specific targets trying to
satisfy their curiosity, which is quite challenging to predict. As a result, explorers
probably may not be satisfied with a strong personalization level, as they would
like to find new topics to grab their attention.
Facilitators: Visitors who are socially motivated. A personalized mobile ap-
plication may not impact Facilitator’s experience in a museum, because they are
primarily attuned to the social aspects of the visit and don’t see the museum
primarily as a place for extensive personal learning and growth.
Professional/Hobbyists: Visitors who feel a close tie between the museum
content and their professional or hobbyist passions. Their visits are typically mo-
tivated by a desire to satisfy a specific content-related objective. Consequently,
strong personalization is a priority for the visitors of this category.
Experience Seekers: Visitors who are motivated to visit because they perceive
the museum as an important destination. Their satisfaction primarily derives
from the mere fact of having been there and done that. They may not require
personalized mobile guides, as their only motivation is to be in a culturally
important place and have a great experience.
Rechargers: Visitors who are primarily seeking a contemplative, spiritual
and/or restorative experience. They see the museum as a refuge from the work-
a-day world or as a confirmation of their religious beliefs. Even if they actually
4 Konstantakis et al.
examine the exhibits of the museum, they often look for that special at-one expe-
rience without any particular interest in gaining additional knowledge. Therefore,
personalization levels for this category should be minimum.
3.2 REPEAT methodology architecture
The cultural heritage experience is being viewed as an ongoing lifelong experi-
ence: curators and cultural researchers are continuously looking at how visitors
can be captured and retained over time, both online and onsite. User personal-
ization can play a major role by reasoning on past experience and other daily
and contextual characteristics of the visitor, making the current cultural her-
itage experience a link in a lifelong chain. This lifelong experience through the
personalized data is the basis of our methodology, which aims in identifying the
user persona with the least possible user input or intervention and customize
mobile guide’s functionality accordingly. The data flow is shown in Figure 1
which depicts the architecture of the methodology.
Fig. 1. The REPEAT methodology
Data acquisition: This stage corresponds to data acquisition from multiple
sources, either explicit or implicit (with respect to privacy and security issues).
REPEAT methodology 5
Those sources can be organized into the following three types: (a) profiled data
derived from mobile device, social media, etc, that allow a refinement of the
associated persona, (b) user input - REPEAT questionnaire which based on our
previous analysis of Falk’s five categories of visitors [5] will let us define how
personalized and obtrusive do the users want their cultural heritage content and
(c) sensor data retrieved from the mobile device’s sensors (during the current or
previous cultural visits) to extract useful information such as their location, if
they are part of a guided tour group, how many times did they visit this location
before etc. [2].
Data reasoning: This stage includes the processing of the multiple data
sources whose wide complexity and diversity will need to be modeled and rea-
soned. The abundance of social media user input and the subsequent opinion
mining on it will allow a better user classification and overall the fusion of het-
erogeneous data streams will associate the user with a customized user persona,
allowing a better understanding of the user’s needs and goals.
Prior studies argue that people can play multiple roles during their interac-
tion with a system which can change over time, either because of interest increase
in a specific topic, or that once the initial experience has been satisfied, users
look for what else is on offer [17]. This behavior is also predicted by REPEAT
methodology, as the user can change the topics of the proposed context anytime.
Also REPEAT not only estimates the main category of each visitor, but also as-
sesses the possibility for all five categories (e.g. Professional/Hobbyist - Strong
possibility, Explorer - Medium Possibility, and Recharger - Low possibility).
3.3 The REPEAT questionnaire
As we described above, Falk identified five categories of visitors based mainly on
extensive post-visit interviews which showed that these identity-related reasons
for visiting museums are a direct reflection on how the public currently perceives
the attributes and affordances of museums; in other words, what the public
perceives as the right reasons for visiting museums [5].
The purpose of REPEAT questionnaire is to estimate the possibility of the
user to belong in one or more of the above categories. Our original selection of
questions which aimed for simple questions that will expose the basic differences
between the five categories is shown on Figure 2.
For example, Facilitators and Explorers usually don’t visit museums to seek
specific knowledge based on the topics of each museum and their interests, since
their first priority is to satisfy other’s interests or their own curiosity respectively.
This feature strongly discriminates them from Professionals/Hobbyists, because
this category almost always enters a museum with a fairly specific goal in mind.
Furthermore, Rechargers, Explorers and Professionals/Hobbyists are typically
repeat-visitors to their local museum while Experience Seekers are first-time
visitors to the museum and many are relatively infrequent museum visitors. Fi-
nally, Facilitators and Experience Seekers are motivated to visit a museum from
their desire for socialization and entertainment, while Professional’s /Hobbyist’s
primary motivation in visiting is typically quite specific knowledge. Meanwhile,
6 Konstantakis et al.
Fig. 2. REPEAT Questionnaire
Rechargers don’t fall into the above categories at all, because they see museums
as places that offer them an opportunity to avoid the noisiness and clutter of
the outside world [4].
After the initial selection of questions, we evaluated the possible answers
and removed questions which may lead us on the same results. Thus, based
on our research, the not aware at all answer at the second question leads to
non-significant results, consequently we ignore it during the final questionnaire
implementation. Subsequently, we ignored the first question regarding the first
time visit, which can be extracted from mobile sensors and user history.
4 Personalized Prototype
We designed and implemented a prototype using Proto.io in which the above
issues will be examined to reach to proper conclusions and estimate the appro-
priate level of personalization in several occasions. Prototypes are commonly
built to serve as explorative artifacts inside a vast design space, and in later
phases as means to evaluate the incrementally formulated design ideas.
In figure 3, the visitor signs in the mobile guide by choosing one of his social
media profiles. As we already discussed, REPEAT uses these profiles to identify
possible interests and thus adjust the context of the mobile guide. Thus, user’s
registration is a basic feature of the REPEAT module. Afterwards, the visitor
answers a questionnaire that will classify him in the five categories analyzed
above. The questions are as few as possible and easy to be answered, as we don’t
want to distract visitors from their cultural experience. Figure 3 also depicts
the personalized content as a result of the REPEAT methodology, based on the
REPEAT methodology 7
Fig. 3. Mobile prototype screens
museum’s topics. The visitor can change his personalization level by selecting
other tasks which are based on the next most probable estimated profile category.
5 Conclusion and future work
Through our REPEAT methodology, we were able to elicit the technology and
user personalization needs regarding cultural areas. This study provides the jus-
tification needed for the implementation of a personalized methodology for a
cultural space that attempts to satisfy the needs of the users. The proposed
methodology will help the visitors understand how and how much the cultural
space can meet and satisfy their individual identity-related needs.
In our study, we have extended the already existing work by designing new
user personalization methods. In retrospect, we trust that our choice of research
methods and process can also be used for the UX evaluation of other technol-
ogy concepts that are futuristic and novel. Also, our contribution will help the
cultural spaces to make changes in the nature of what they offer to the public
that are more consistent with the perceived needs of their visitors and increase
their institutional value by applying the principles of the proposed module to
everyday practice.
In the future, we will use this personalized methodology as a basis to design
the frontend and backend components of the REPEAT module. Furthermore,
our next step is the implementation of the module in a cultural mobile applica-
tion of an institution and a formative evaluation with prospective users and a
redesign cycle to ensure that the mobile user interface adequately supports user’s
needs, and also check the efficiency, validity and reliability of the questionnaire
8 Konstantakis et al.
proposed. The module will be implemented with a strong feedback mechanism
that will request user opinion about ease of use, potential usefulness, persona’s
identification accuracy user satisfaction and intrusion level etc., allowing for a
semi-automated customization procedure towards a more satisfactory Cultural
UX overall.
6 Acknowledgments
The research and writing of this paper was financially supported by the General
Secretariat for Research and Technology (GSRT) and the Hellenic Foundation
for Research and Innovation (HFRI). John Aliprantis has been awarded with
a scholarship for his PhD research from the 1st Call for PhD Scholarships by
HFRI Grant Code 234.
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