=Paper=
{{Paper
|id=Vol-2235/paper9
|storemode=property
|title=Cultural Location Touring Framework: A Roadmap Based on QoE Modeling and Visitor Real-Life Behavioral Choices
|pdfUrl=https://ceur-ws.org/Vol-2235/paper9.pdf
|volume=Vol-2235
|authors=Symeon Papavassiliou,Athina Thanou,Eirini Eleni Tsiropoulou
|dblpUrl=https://dblp.org/rec/conf/euromed/PapavassiliouTT18
}}
==Cultural Location Touring Framework: A Roadmap Based on QoE Modeling and Visitor Real-Life Behavioral Choices==
https://ceur-ws.org/Vol-2235/paper9.pdf
Cultural Location Touring Framework:
A roadmap based on QoE modeling and visitor
real-life behavioral choices
Symeon Papavassiliou1, Athina Thanou1 and Eirini Eleni Tsiropoulou1
1 Institute of Communication & Computer Systems, School of Electrical and Computer Engi-
neering, National Technical University of Athens, Zografou 15452, Greece
papavass@mail.ntua.gr, athinathanou@central.ntua.gr,
eetsirop@netmode.ntua.gr
Abstract. In our work and consideration cultural heritage spaces, and particularly
museums, are considered as dynamic social systems, where visitor choices and
decisions are interdependent and constrained. Under such a setting, and taking
into account that each visitor aims at maximizing his own satisfaction, we ini-
tially formulate quantitative approaches and functions that capture and reflect
visitor obtained Quality of Experience (QoE) from his touring experience, ac-
cording to several socio-physical and behavioral factors. We further discuss and
demonstrate how such a QoE modeling approach, can be used in order to improve
and optimize various operation aspects of the cultural touring, both from visitor
as well as museum operator points of view, while focusing on providing custom-
ized and personalized experience in visitor touring. In addition, we highlight how
to treat the issue, that in practice visitors are not acting as neutral utility maxi-
mizers as commonly assumed, but often present risk seeking behaviors in their
touring decisions. The latter affects significantly the various decisions making by
the visitors and the corresponding obtained satisfaction, while provides useful
guidelines for the museum operators with respect to their service offerings.
Keywords: Quality of Experience, risk-preferences, visitor behavior modeling,
museum time management, congestion management, museum touring.
1 Introduction
Cultural heritage spaces, and in particular museums, are becoming dynamic envi-
ronments in the service of the society aiming at reconnecting with the public and
demonstrating their value and relevance in contemporary life. Several recent statistics
[1-2] have made evident that museum exhibition draws the crowd, offering multiple
experiences to the visitors. However new techniques are required towards actively en-
gaging the museum visitors in a participatory manner towards improving their visiting
experience. One of the most fundamental questions that arises in cultural spaces is:
“Which is the most efficient methodology for enhancing cultural heritage spaces visit-
ing experiences?”
Cultural Informatics 2018, November 3, 2018, Nicosia, Cyprus. Copyright held by the
author(s).
76
In principle, in our consideration the evolving cultural heritage space environment
is viewed as a Cyber Physical Social System, where visitors evolve in an environment
that induces several constraints and interdependencies [3]. The visitor evolves in a
physical or virtual space with others, where their behavior and decisions are constrained
by the former, while influencing and being influenced by the latter [4]. The intuitive
key principle followed in our consideration and work – considering visitor point of view
- is simply stated as: “Making the most of your cultural heritage space visit”. Neverthe-
less, taking into account the cultural heritage space operator point of view, significant
insights are obtained that can contribute to the following principle: “Knowing your vis-
itors is an essential part of building your audience and better planning your services”.
In a museum, as opposed to other social environments, such as in a church or a
school, the tempo of the experience is primarily controlled by the visitor himself, there-
fore a heavy burden regarding the visiting related decision making falls upon the visitor.
More importantly, a specific visitor’s decisions with respect to several actions during
his touring in the museum - ranging from determining which exhibit to visit up to opti-
mizing the time to spend to each exhibit - are either explicitly or implicitly interdepend-
ent on the actions taken by other visitors that are present at the same time in the cultural
heritage space.
Two different key research directions are identified towards addressing these major
challenges, and offering an enhanced personalized museum visitor realistic experience.
The first one refers to efforts associated with proper modeling of Quality of Experience
(QoE), both qualitatively and quantitatively. Based on such QoE formal modeling,
methodologies that improve the operation aspects of the cultural touring, both from
visitor as well as museum operator points of view, are discussed. The second one deals
with the critical aspect of properly using visitor behavioral insights, and more im-
portantly incorporating behavioral factors into various operation processes and ap-
proaches, to examine and improve human cultural experiences. In the following we will
introduce the challenges faced by each one of these directions, while providing insight-
ful concepts, methodologies and our research approaches and designs, towards treating
them, focusing primarily on the use case of museums.
2 Visitor Experience Modeling and Optimization
2.1 Quality of Experience Modeling
Visitor experience and satisfaction becomes a critical aspect of the effectiveness of
various optimization approaches and decisions required through a visitor touring in a
cultural space. Several research efforts have been devoted to the study of museum vis-
itors’ perceived satisfaction [5-6]. These works argue that visitors’ satisfaction can be
affected by several factors, both physical and cultural – either common or personalized,
which may not influence visitor’s experience in the same manner and with equal
weight. Visitor satisfaction, often referred to as QoE, is a subjective metric that refers
to both the visitor personal style and characteristics, as well as the specific context un-
der consideration. The proper treatment and formulation of those factors in well-struc-
tured expressions/functions reflecting museum visitors’ QoE is of high importance in
order to understand, predict and optimize visitors’ QoE. The research works, presented
77
in [7] through a qualitative analysis, and in [8] through a quantitative validation, iden-
tified four basic visiting styles in order to capture visitors’ preferences and interests.
Four animal metaphors, i.e., ant, butterfly, fish and grasshopper, have been respectively
adopted to better demonstrate the corresponding visiting styles’ attributes.
Furthermore, several additional works emphasizing on the sociocultural aspect of
museum visitors’ QoE, have been presented in the literature, including [9] where mu-
seum visitors’ behavior is studied towards evaluating the impact of exhibitions on vis-
itors’ satisfaction, and [10] that relates visitors’ emotions with the corresponding QoE.
A first attempt to formulate museum visitors’ QoE in mathematical functions was pro-
posed in [11] considering visitor’s distance from the exhibit and his / her stop over time
to the exhibit. Furthermore, the authors in [12] studied the effect of smart routing and
intelligent recommendations on improving museums visitors’ QoE.
In our work in [13-14] a holistic approach to the formulation and optimization of
QoE functions of the visitors was introduced, identifying the most influential parame-
ters that affect the QoE notion. Towards relating the physical parameters to visitors’
perceived QoE five main parameters have been identified as detailed and documented
in the literature [7, 11-12]. The five main parameters are: distance between exhibit and
visitor, distance between two sequential exhibits, crowd density, time spent with facil-
itator providing useful information to the visitors about the exhibits, exhibition size.
Taking into account that the aforementioned parameters do not influence visitor’s QoE
in the same manner, we have developed a questionnaire [15] addressed to experts (e.g.
archeologists, museum and gallery directors, etc.) towards determining the importance
of each parameter, as well as obtaining some critical values of these parameters in order
to design at a second step the formal QoE functions. Based on this, a human-in-the-
loop approach was proposed in [14] towards determining a physical, personal and in-
terest-aware museum touring methodology that maximizes visitor’s QoE. Furthermore,
a self-organizing mechanism for forming museum visitor communities was introduced
[16], which exploits the visitors’ personal characteristics and social interactions, and
which aims at enhancing visiting experience based on a participatory action research
process.
2.2 QoE-Based Recommendation Selection and Time Management
However, the aforementioned efforts have focused on quantifying and optimizing
visitors’ perceived QoE, mainly expressed via physical context parameters (e.g., mu-
seum’s size, placement of the exhibits, etc.). They do not properly consider the impact
of the visitor’s choice, in selecting among a set of recommendation services made avail-
able to him/her by the museum, as well as determining his/her optimal visit time, in
view of the available services. Therefore, we propose to address this problem in a for-
mal and unified manner.
Specifically, we rely on the iterative design and adoption of: i) a machine learning
framework to treat the problem of intelligent recommendation selection, and ii) a game
theoretic approach to determine museum visitors’ optimal visiting time, driven by the
visitors’ QoE optimization in the museum. The latter consideration is motivated on one
hand by the distributed nature of the optimization problem under treatment and the
selfish behavior of the visitors in terms of maximizing their own perceived QoE, and
78
on the other hand by the fact that decisions of the various visitors are interrelated. The
visitors are modeled as learning automata, adopting a machine learning mechanism and
using a learning process to select the most appropriate recommendation to perform their
museum touring. Each type of recommendation available to the visitors offers different
levels of QoE, for example from offering them a simple map for a self-guided tour to a
guided visit in their own language. The visitors are able to intelligently sense their en-
vironment (e.g., actions of other visitors) while keeping a history of their own decisions
in order to make more educated and advantageous actions in the future, as time evolves,
and finally, converge and select the type of recommendation that will improve their
perceived QoE. Given the museum visitors’ actions in terms of recommendation selec-
tion, each museum visitor aims at determining his/her optimal visiting time in order to
maximize his/her perceived QoE. This is formulated as a distributed maximization
problem of each museum visitor’s combined QoE function with respect to his/her vis-
iting time. Considering the distributed nature of the optimization problem and the self-
ish behavior of the visitors in terms of optimizing their own perceived QoE, a game
theoretic approach is adopted towards determining its solution.
This overall process is an iterative one, where the output of the visiting time man-
agement problem feeds the learning system in a recursive manner (Fig. 1) in order to
build knowledge and conclude to the optimal recommendation selection, even if
changes apply to the system as it evolves. The necessary information in order to take
their decision is their visiting time, the corresponding perceived combined QoE values
at the previous time slot of the machine learning framework, and the penetration of each
recommendation within the visitors’ pool. The latter (i.e. reward probability) is ex-
pressed via the ratio of the total QoE achieved by visitors who selected a specific rec-
ommendation, over the total QoE achieved by all museum visitors who are present in-
side the museum at the examined timeslot.
Fig. 1. QoE-based joint recommendation selection and visiting time management
3 Incorporating Visitor Behavioral Factors and Real Life
Choices in Decision Making
As mentioned before, cultural heritage sites, such as museums, are reflections of
society as they are repositories of historical, precious and significant objects and arti-
facts [17]. Therefore, observation of the behavior of visitors within a cultural heritage
space area makes it possible to identify some particularly important issues and phenom-
ena that occur during a touring experience.
79
In particular, with respect to congestion and popularity, museum exhibits can be
classified in two main groups: a) safe exhibits, that typically correspond to less con-
gested ones, where the visitors are assumed to receive guaranteed satisfaction propor-
tional to the effort (e.g. time) investing in them, and b) common pool of resources
(CPR) exhibits, which are the most popular ones with possibly increased congestion
and uncertain outcome in terms of visitor satisfaction. CPRs belong to a broad class of
goods that share two common characteristics [18]. They are non-excludable, thus it is
impossible to exclude someone from benefiting or accessing the good and they are sub-
tractable meaning that the consumption by one user reduces the ability of being used
by another. With reference to the latter, it is absolutely true that it is not feasible to
prevent a visitor from observing an exhibit, and the more time a visitor spends observ-
ing an exhibit, the less available this exhibit becomes to the rest of the visitors. There-
fore, an exhibit constitutes a resource that experiences negative externalities and its
extensive “usage” may lead to the “failure” of the exhibit.
Respecting the need for distributed, autonomous and scalable solutions and algo-
rithms, the focus has been placed on the study of non-cooperative paradigms (e.g. game
theory as described above) where decisions are taken autonomously by the visitors,
which though may not directly interact or explicitly cooperate with each other, they
become interdependent. The majority of existing approaches have relied on the princi-
ples of Expected Utility Maximization, where visitors aim at selfishly maximizing their
own degree of satisfaction (i.e. QoE), as expressed through various forms of utility
functions. Nevertheless all the methodologies and approaches used so far, have not
managed to properly address the fact that individuals in real life do not necessarily
behave as neutral expected utility maximizers, but they tend to exhibit risk-seeking or
loss aversion behavior especially under uncertainty.
To deal with this challenge, we exploit Prospect Theory [18-23] in order to integrate
risk preferences in the involved utility function, depicting deviations in decision mak-
ing due to risk seeking or loss aversion that traditional models fail to capture. Following
the aforementioned exhibit classification, in our work Prospect Theory is adopted and
applied to determine visitor optimal visiting time in museum exhibits and evaluate the
achieved QoE. Prospect Theory is one of the most widely accepted behavioral models
of user decision-making under probabilistic uncertainty. It is the most accepted descrip-
tion of how people assess probabilities and utilities at outcomes and how people com-
bine these two in gambles or competitions [20]. Visitor losses or gains are measured
with respect to a reference point, which is defined as the ground truth based on the
common sense of human behavior, while are possibly rated differently and not linearly.
Furthermore, overconsumption of a resource in many cases has regularly been associ-
ated with a subsequent failure of the resource (i.e. CPR exhibits), with the investors
eventually receiving negative returns from their initial investment. Prospect theoretic
solutions will be formulated via a utility function (Fig. 2) associated with visitors´ re-
turns or satisfaction by investing in a resource according to the following equation:
° z z0 a , z t z0
U z ® E (1)
°̄ k z z0 , z z0
80
where z n
,n is user’s perceived utility and z0 n
is a reference point, which
acts as the ground truth for each visitor. The parameter k , k represents the idea that
the loss curve is usually steeper than the gains curve, thus quantifies visitor sensitivity
to losses as compared to gains, while by properly tuning parameters a, E we can
determine the extent of the non-linearity in the corresponding utility curves, illustrating
the relative sensitivity to gains and losses of small magnitude compared to those of
large magnitude. It is highlighted that parameters a, E , k can be visitor specific satis-
faction related parameters and characterize in a unique and personalized manner each
visitor i, i N , i.e. ai , Ei , ki , and thus can be exploited towards providing the possibility
for a more personalized visitor’ satisfaction treatment.
Fig. 2. Prospect Theoretic Utility
In a museum, the visitor will be able to invest time either in a safe resource, charac-
terized by fixed gain or to a common pool of resource (CPR), characterized by low cost
and higher than the safe resource – usually less predictable - gain. The CPR rate of gain
return decreases in the total investment of the resource. The above model can be for-
mulated and solved as a common pool resource (CPR) game where users select to invest
between a common (shared) and a safe (standard) resource, with different returns from
each resource type. Following this example, the equation below summarizes the choices
of safe resource and CPR, and it will be combined with equation (1) to formulate the
visitor-centric QoS-aware distributed resource management problems:
°g e, if xi 0
Ui x ®
°̄g e xi xi r xT , if xi z 0
(2)
where g is the gain per time unit investment received from investing in the safe re-
source. The visitor has a total available budget of time e and invests xi to the CPR,
which is characterized by r xT rate of return, which is decreasing with respect to the
N
total investment by all the visitors, i.e., r xT ¦ x , where N is the total number of
i 1
i
visitors. It should be highlighted that if the visitor invests a lot of time in the CPR ex-
hibit, then the CPR’s failure probability increases. In the case of CPR failure, the visi-
tor receives no return from it.
81
4 Open Issues and Future Steps
As mentioned before, part of our current and future work focuses on studying the
behavior of museum visitors in terms of recommendation selection and improving their
perceived QoE, under the risk averse and risk seeking aspect of their decision-making
process. As current models do not properly address the fact that individuals in real life
do not necessarily behave as neutral expected utility maximizers especially under un-
certainty, integrating risk preferences in the involved utility function depicting such
deviations in decision making is of high research and practical importance.
Under this perspective, the concept of announcing different pricing policies per rec-
ommendation will be examined as an incentive mechanism provided by the museum to
the visitors, in order to deal with the congestion control, the routing of the visitors
within the museum, and the overall planning of the visiting traffic and touring. Addi-
tionally, innovative intrinsic and extrinsic motivation mechanisms will be devised and
paired with the aforementioned incentive mechanism to improve the word of mouth
reputation of the museum, increase the revisit and engagement of the visitors and sup-
port the smooth operation of the museum and increase of its profits.
It is also of high practical significance to investigate how framing effects can influ-
ence or even drive visitor route and overall touring decisions, by appropriately and in-
telligently providing people with options within the context of a specific frame, while
at the same time enable cultural heritage site operators to properly plan the visiting
traffic within the cultural heritage site.
Finally, our future plans include the execution of real experimentation with actual
museum visitors in order to validate the proposed framework and its applicability under
realistic conditions. Context-awareness and the Internet of Things (IoT) infrastructure
and intelligence offer tools that can significantly promote our proposed research frame-
work to the actual market. For example, specific measurements regarding visitors’ be-
havior can be acquired and considered in our methodology, via the support of cameras
and video processing techniques. As a result, the integration of the overall proposed
museum touring framework with real-time information obtained through an IoT infra-
structure will allow the dynamic adaptation of the proposed approaches to the actual
museum conditions, towards realizing an IoT-based smart museum for a new interac-
tive cultural experience.
Acknowledgements. This research effort is supported by ICCS Research Award under
Grant Number 65020601.
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