It’s a Match! A Knowledge based Recommendation System for Matching
Technology with Events
Genildo Gomes, Isabelle R
ego, Moises Gomes, J
ulia Conceic¸
ao, Artur Andrade, Tayana Conte,
ıs Castro and Bruno Gadelha
Institute of Computing, Federal University of Amazonas, Manaus, AM, Brazil
Recommendation Systems, Interaction Technology, Events Planning.
The use of technologies to promote interaction and engagement at events is part of modern entertainment. In
the different types of events, there are different technologies to increase this interaction. In scientific events, for
example, organizers use voting platforms with the public; in music festivals, LED bracelets and the flash light
of the smartphone are used; while in cultural and sports events, there are digital cheer leading thermometers.
Considering the search by experts in the field of events and entertainment for new technologies, in this study, a
recommendation system is proposed that relates different classification aspects of events in order to suggest a
list of appropriate technologies for that event, based on knowledge bases built by the experience of experts. The
proposed solution was evaluated through acceptance studies using the technology acceptance model (TAM),
and interviews with six experts with experience in the area of production and organization of various events.
Results indicate that users intend to use the platform to assist in the definition of technologies due to its
innovative factor, among other information discussed in this paper.
Events are unique experiential products that can pro-
duce a set of sensations, emotions and engagement
with participants (Ayob, 2011). The event industry
constantly seeks to provide differentiated, interactive
and innovative experiences in order to engage its spe-
cific audience. Such events can be represented as in-
dividual celebrations, such as a birthday party, to mu-
sic festivals, conferences or large sports events. In
these events, visitors have the opportunity to experi-
ence unique moments, and this generates interest in
new sensations, emotions, leisure and sociocultural
experiences outside their daily routines (Jago, 1997;
Getz et al., 1997).
In general, events have several distinct aspects that
reflect their own characteristics, such as the category
of the event (Getz et al., 1997), the type of audience
or how they manifest themselves (Mackellar, 2013).
Recently, technologies that seek to take advantage
of the qualities of these events in order to better or
more fully engage or interact with the public have be-
come popular. This can be observed at festivals or
concerts of famous bands, events in which the inter-
active use of lights is common, whether through LED
bracelets (Burns, 2016) or smartphones (Vasconce-
los et al., 2018). While in educational events, the
use of audience response systems (ARS) is predom-
inant (Nelimarkka et al., 2016). In the case of cultural
events, Martins et al. (Martins et al., 2020) designed a
competitive and collaborative application for the dis-
pute between two groups of supporters.
In this sense, in planned events there is always the
intention to create and shape the individual and col-
lective experiences of the public, in order to gener-
ate greater involvement among the participants (Getz,
2007). However, event organizers do not have a
specific technological strategy, which allows them to
gather the qualities of different types of events, and
recommend technologies to achieve the objectives or
meet the demands of a given event, while aiming for
greater interaction with their audience.
Thus, the study presented in this paper aims to
help event organizers to plan greater interaction with
their audience through the recommendation of tech-
nologies. For this, we used a database with thirty five
technologies obtained from the literature and industry
. These technologies were classified based on
their own characteristics combined with the charac-
teristics of the events where they can be used (Gomes
Gomes, G., Rêgo, I., Gomes, M., Conceição, J., Andrade, A., Conte, T., Castro, T. and Gadelha, B.
It’s a Match! A Knowledge based Recommendation System for Matching Technology with Events.
DOI: 10.5220/0010453605250532
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 525-532
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All r ights reserved
et al., 2020).
This database served as a support for the devel-
opment of Techs4Events, a platform for recommend-
ing technologies that expand the participation, inter-
action and engagement of the public in different types
of events. Techs4Events takes into account the char-
acteristics of events and their audiences as factors in
a calculation, and thus generates a compatibility rate
between technologies for events.
In order to evaluate the platform, we conducted
experimental studies with six experts with experience
in the area of production/promotion of different types
of events. For this, a questionnaire based on the tech-
nology acceptance model (TAM) was applied in order
to validate aspects such as usability, ease and inten-
tion to use the platform (Davis, 1989). The result of
the analysis of the responses using the TAM method
indicated that the tool fulfills its role efficiently, but
that the criteria that influence the recommendation
need to be improved. In addition, it was found that
the system is easy to use, thus generating satisfactory
results for the tests performed.
In this paper, concepts related to event types, audience
types, recommendation systems and different tech-
nologies were used to build on ideas and develop so-
lutions for the problem. The following subsections
detail these concepts further.
2.1 Events, Audience and Technology
An event is a meeting of a group of people, whether
with specific interests or not. In the context of en-
tertainment, there are several types of events, but
in this paper, the types of events were based on
the typology proposed by (Getz, 2007). The fol-
lowing types of events are considered in this work:
arts and entertainment, cultural celebrations, private
events, recreational events, commercial events, edu-
cational/scientific, political/state, and sports and com-
petition events. Each type of event has unique par-
ticularities and provides distinct experiences for the
Given the distinct characteristics of the events,
the audience that comprises and participates in these
events also has distinct objectives. Every event has
a corresponding audience type, seeking to enjoy the
event in different ways. In the context of this paper,
the audience is associated with the criteria of relation-
ship with technology and event.
The type of audience used for this association was
catalogued by Mackellar (2013). In it, Mackellar de-
fines audiences based on their goals, for example, in
mass, special interest, community, incidental and me-
dia. Consequently, different events can support audi-
ences that rely on the nature of the event to engage,
such as fans wearing the team shirt at football games
or singing along to the crowd (Ludvigsen and Veera-
sawmy, 2010).
Technology has the potential to expand the possi-
bilities of audience interaction with the various types
of events. In the literature, studies that use the smart-
phone as a mediator between audience and event are
increasingly common. As an example, the work of
Sheridan (Sheridan et al., 2011), which aimed to an-
alyze collaborative actions using measures of mutual
engagement in festivals through an application called
Another work that uses the smartphone for inter-
action is massMobile (Freeman et al., 2015).This con-
sists of a web app (web application accessible by mo-
bile devices) that allows the active participation of the
audience of musical performances, sending texts, par-
ticipating in voting, drawing and creating geometric
figures, among other features.
The importance of highlighting these points re-
flects on how interactive experiences influence audi-
ence engagement and the role of technology in this
process. In addition, technologies nourish the knowl-
edge base used in this paper. In order to facilitate
access to these technologies, the concepts of recom-
mendation systems were used and are presented in the
following subsection.
2.2 Recommendation Systems
This study proposes the development of a system ca-
pable of suggesting technologies for events. For this,
we used the concept of recommendation systems, de-
fined as software tools or techniques that provide sug-
gestions of items that are useful to a user (Ricci et al.,
The way these systems can provide these sugges-
tions stems from the use of various data sources to be
able to infer the interests of the individual to whom
this recommendation is being provided. In the lit-
erature, there are several types of recommendation
systems, however, according to Aggarwal (Aggarwal,
2016), there are five basic models: collaborative sys-
tems, content-based systems, knowledge-based sys-
tems, demographic systems, and hybrid models.
Based on the description of Aggarwal (2016) re-
garding knowledge-based recommendation systems
and, specifically, the category of those that are
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
constraint-based recommender systems, it is im-
portant to highlight the similarity that it has with
Techs4Events. First, both follow the same principle of
requiring the user to specifically explain their require-
ments to be able to determine the restrictions to be ap-
plied on the items that will be returned to them. That
is, both make use of knowledge bases that contain
rules that map the consumer’s requirements (which in
this case would be the description of the event in the
form) with the product attributes (which would be the
recommended technologies).
However, the difference between the two is that
while in constraint-based systems only results that fit
the restrictions applied by the user are returned (Ag-
garwal, 2016), in Techs4Events, all results available
in the knowledge base are returned and ordered in de-
scending percentual order.
Regarding the category of case-based recommen-
dation systems, the similarity is found in how the
results are shown. Both recommendation systems
return and rank items by similarity to user require-
ments. However, the difference lies in the user’s abil-
ity to criticize the results in case-based recommen-
dation systems. That is, these users can select one or
more of the results and specify new searches for items
that are similar to these, but specifying the attributes
that they want to be different (Aggarwal, 2016). In
the Techs4Events recommendation system, this user
feedback is not possible.
Besides, the way the user interacts with the system
resembles search-based interaction, which consists of
a system in which the user’s preferences are extracted
using a predefined sequence of questions (Aggarwal,
2016). In the context of Techs4Events, this occurs
through the form, in which there are predefined ques-
tions (a type of audience, type of event, among oth-
ers), and the user explains the type of technology they
will need when describing their event by selecting the
answers to these questions.
In order to obtain the knowledge to develop the so-
lution to the problem presented in this paper, the
methodology adopted was descriptive, since existing
market data, scientific literature review and techno-
logical approaches are combined, with the aim of
generating strategic information to support decision-
To carry out this research, we followed, as a mech-
anism for data collection, two phases, named here (i)
exploratory and (ii) experimentation. To perform the
analysis on the relationships of the object of study
(events) with the recommendations of technologies
of the proposed recommendation system, we used a
phase here called (iii) acceptance. The objectives of
this descriptive research consist in the representation
of the relationship between technologies for engage-
ment and their effectiveness in the application in dif-
ferent types of events. The phases described below
represent the process to achieve the goal.
The exploratory stage (i) consists in reviewing the
literature in order to accrue new technologies used for
public engagement in events, and further the knowl-
edge regarding the characteristics of different types
of events, and the creation and classification of rec-
ommendation systems. An outline of this step is pre-
sented in Section 2, Background.
From the knowledge obtained, we proceeded to
the stage of (ii) experimentation, which consists in
the application of the knowledge obtained in order to
generate the solution to the problem. In this paper,
this step is summarized in the creation and refinement
of a web application (Techs4Events). The experimen-
tation step also represents the implementation of a set
five heuristic rules as used in Gomes (Gomes et al.,
2020), in which the criteria for classification of tech-
nologies are built into Techs4Events.
The (iii) acceptance stage consists of studies on
six users with experience in organizing or producing
events. Users were encouraged to use Techs4Events
for a short period of time, and to evaluate acceptance
via a questionnaire based on the technology accep-
tance model (TAM) (Davis, 1989) and, afterwards, an
interview with three open-ended questions was per-
formed. The results of this step are presented in Sec-
tion 6.
Based on the triad of event, technology and audience,
we propose a solution that is also based on technol-
ogy to recommend technologies capable of expanding
the engagement and interaction of the audience in the
different types of events. This solution comes from a
recommendation system called Techs4Events, whose
operation is based on identifying with the experts the
characteristics of the event that they are planning and
compare them with the characteristics of technologies
intended for different types of event.
It’s a Match! A Knowledge based Recommendation System for Matching Technology with Events
4.1 Criteria and Subcriteria
To guide each technology to a given event, we based
our method on the existing proposal of Gomes (2020),
which reports characteristics and relationship of dif-
ferent events in contrast to technologies. In it, each
technology is classified according to a series of crite-
ria, among them, the types of events (Getz, 2007) sup-
ported by that technology and types of audiences that
can enjoy that technology (Mackellar, 2013). Other
five criteria are focused on the behavior of the audi-
ence and the general context of the event. Addition-
ally, these criteria have options (subcriteria) that rep-
resent this relationship in more detail.
In the context of events, there are technologies that
can take advantage of the position of the audience in
the event space. The Audience disposition criterion,
deals with how the audience is organized in the event,
being it in fixed positions, referring to an audience
allocated in previously defined seats/locations (e.g.
cinemas, theaters, seats marked in football games)
or random positions, in which the audience does not
have fixed positions in the event space, not occupying
a specific space or seats (e.g., Times Square, Rock in
Rio, music festivals in general).
On the other hand, the Network infrastructure
criterion allows us to expand the possibilities of tech-
nologies, in the sense that there is the possibility of
the event allows the public to download an applica-
tion, for example. In this perspective, there are two
possibilities whether it has infrastructure, referring to
free access to the internet in the event or does not have
infrastructure(not required), referring to the absence
of internet access in the event.
The audience can behave in two ways in the event,
in this sense, the Audience behavior criterion reflects
precisely in this behavior, i.e., when the audience is
active, when it demonstrates behavior that influences
the event or when it has an interest in new experi-
ences. It can also be passive, if we refer to the listen-
ing audience, or acting as a spectator, being there just
to watch and enjoy the event (Mackellar, 2013).
Audience interaction is a criterion that defines
when the interaction can or should happen during the
event. Typically, technologies are seen as an instru-
ment and not necessarily the center of attention, and
are often designed to be used only at specific times.
This is to be the decision of the organizer and, in this
case, it can occur in two ways. It can be planned,
in which the interaction with the event is previously
combined or elaborated by its organizers. As such,
the audience engages and interacts as foreseen in a
script at previously defined moments of interaction.
Or it can be unplanned; the audience interacts organ-
ically, since there is no specific script or moments for
interaction. Reactions take place according to the de-
gree of audience involvement with the event.
The Venue of the event presents an important
point of view, due to the possibility of the event be-
ing interrupted. It is important to know the types of
technologies that can be used in the chosen place, and
the subcriteria of this category are open, referring to
events that occur in open places, with a view to the sky
and ample space for the circulation of the audience or
closed places with a physical structure that has a cov-
ering to host the audience, without the possibility of
suffering from adverse weather conditions that may
cause a temporary pause, or permanent interruption
of the event.
Each of these criteria was used to calculate the ap-
proximation that the event has with each technology.
Such a calculation served as the basis for the creation
of a prototype.
4.2 The System
From the idealization of the problem, prototypes were
developed for a better visualization and evaluation of
the proposed solution, and later a web application,
which became the first version of Techs4Events. Fig-
ure 1 presents the home screen of Techs4Events in
desktop layout.
Figure 1: Techs4events home screen.
To use Techs4Events, the user follows a simple proce-
dure that starts by accessing the system and clicking
on the button “Discover Technologies” on the home
page. After that, the user informs the name and type
of the event and clicks the “next step” button. The
second part of the form is presented, in which the user
must inform the specific characteristics of the event,
such as network infrastructure. Finally, the user clicks
the “Search” button to check the recommended tech-
At this point, the system calculates how many op-
tions (subcriteria) the user has selected and thus de-
termines what the maximum score would be that a
technology could get. After that, it is checked how
many of these subcriteria each technology was as-
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Table 1: Example of how the recommendation is made.
Subcriterion selected
by the user
Subcriterion that
technology has
Venue Open Open 1
Not required Not required 1
Fixed Fixed, Random 1
Audience behavior
Active Passive 0
Audience interaction Planned Planned 1
Type of event
Arts and
Cultural celebration
Type of audience
special interest
sociated with. For each subcriteria that the technol-
ogy was associated with, 1 point is added to its total
score. The only exception to the rule happens when
the user selects the subcriterion “has” in the “network
infrastructure” criterion, indicating that the place of
the event provides internet connection. In this case,
1 point is added both in technologies that use the net-
work infrastructure and in technologies that do not use
it, considering that the presence of a network would
not exclude tools that do not need it. An example of
how the recommendation is made can be seen in the
Table 1.
After this process, the ratio between the score
obtained by the technology and the maximum score
is calculated, and following this, the result is multi-
plied by 100 to obtain the percentage. At the end,
all technologies are shown to the user in descending
percentual order of compatibility, while the end user
checks the list of recommended technologies by ex-
ploring their descriptions, websites, and compatibility
The Tech4Events recommendation system followed
the methodological path described in Section 3, con-
sisting, for reasons of research organization, of three
phases. This section describes Phase (iii), related to
the descriptive research analysis, in the form of vali-
dation of Tech4Events by six entertainment experts.
5.1 Preparation
In order to prepare for the validation of the
Tech4Events, several meetings were held for general
improvement of the prototype before the studies, such
as interface improvements and database adjustments.
In addition, the questions for the acceptance question-
naire were defined based on the TAM model (pre-
sented in the evaluation section), and the informed
consent form (ICF) was validated.
Basically, the TAM model is intended to ana-
lyze the behavior and motivation of users regarding
the characteristics of the system (Davis, 1989). The
model uses two factors to evaluate the user: perceived
usefulness, which seeks to know how much the user
believes that the use of the system will improve their
performance; and perceived ease of use, which seeks
to analyze how much the user believes that the use
of the system will be effortless (Venkatesh and Davis,
The study participants were selected according to
two criteria: they were older than 18 years and had
experience in organizing or producing events. The
participants answered two questions to evaluate the
intention of use in order to understand the level of in-
fluence of the software in everyday life. Additionally,
the questions follow the affirmative questions model,
for this, users can express their answer following a
Likert scale of agreement ranging from 1 to 7, where 1
corresponds to “strongly disagree” and 7 to “strongly
agree”. Due to the pandemic, the questionnaire was
applied remotely, using the Google Forms platform.
Also due to the pandemic and a conflict of schedules
with users, the tests were scheduled days before, and
carried out by Google Meet, an online platform for
video calling.
5.2 Execution
All the surveys carried out followed a series of four
steps. At each beginning of the survey, the consent
of the users to record the meeting was requested. The
first stage began with a short presentation by the me-
diator about the idea behind Techs4Events and how
the stages of the surveys will take place. The sec-
ond step is the use of Techs4Events by the user, in
it the user is asked to share the screen of the device
used during the test of Techs4Events. From this mo-
ment on, each user is free to express themselves, ask
questions and make criticisms about the application.
The third stage occurs after the test, where the user
is asked to answer the acceptance questionnaire. The
fourth stage consists of an interview with three open
questions. The following questions were asked: 1 -
What was the biggest problem you encountered when
using it? 2 - Would you use the technologies recom-
mended by this in an event? Why/Why not? 3 - What
are the positive and negative points of the application?
It’s a Match! A Knowledge based Recommendation System for Matching Technology with Events
In this study, the target audience was people who
already have experience in organizing or producing
some type of event. In total, the study collected data
from six people who work in this context. The Table
2 presents the data of each participant together with
which category of events they usually hold.
Table 2: Demographic data of participants.
Participants Age Group Sex Type of events they usually hold
Amount of
Participant 1 31 - 35 M
Arts and entertainment
Private events
5 to 10 years
Participant 2 41 - 45 M
Arts and entertainment
Educational and scientific events
Private events
Recreational events
more than 10
Participant 3 46 - 50 M
Arts and entertainment
Educational and scientific events
Private events
Recreational events
more than 10
Participant 4 46 - 50 F
Educational and scientific events
Private events
0 to 5 years
Participant 5 18 - 25 M
Educational and scientific events
Cultural celebrations
0 to 5 years
Participant 6 41- 45 F
Educational and scientific events
Private events
0 to 5 years
The TAM evaluates the acceptance of the tool in terms
of aspects such as the usefulness and intention of use.
The results of the application of the TAM are pre-
sented in Figure 2, where it is possible to see the per-
centage of how the participants scored a certain ac-
ceptance item. The colors in Figure 2 differ according
to the points of the scale, in which the most positive
variation is represented by the variation of the green
color and the most negative tends to more reddish col-
ors. When the answer is neutral, it is represented by
the gray color.
The interesting thing that can be analyzed through
TAM is the perspective of the positive and negative
aspects of the response of users. The first step of
the validation questionnaire corresponds to the topic
“perceived usefulness - PU” of TAM and has four af-
firmative questions. The second stage of the ques-
tionnaire corresponds to the “perceived ease of use
PEU” of the model. The last step of the questionnaire
sought to directly evaluate the ”intent to use - IU” of
In the acceptance study reported here, considering
the TAM questionnaire approach to evaluate the ac-
ceptance of Techs4Events, some elements need to be
highlighted in relation to the opinion of the experts.
The participants mostly seem satisfied with the results
of the Techs4Events, however, when questioned in the
interview or during the study, they highlighted some
characteristics of the system as being negative points.
The first of these is the list of recommended tech-
nologies, for which some comments highlight that
the highest percentage of some technologies did not
match the entered criteria of the event, and this can be
seen in the quotes: U1- “So I think I had no problem
with the interface itself,..., I think it was missing an
option to define a little better the type of event that
each holds in order to come up with a suitable tool”,
U4 “I do not know what was the answer that led to
these first two technologies, but I do not know if it
combines”. These comments may be associated with
the item (PU1) of the figure, which stands out for be-
ing the only item with a reddish variance. Thus, it
was expected that the ratings related to utility would
be lower, since more than one user highlighted this
aspect as a negative point.
However, mostly represents positive satisfaction
of the public, since positive comments on the recom-
mended technologies are also taken into account. For
example, the comment of U3 highlights “Having this
in one place is a wonder. It is great for those who
are organizing events”, while U4 claims “You have a
place where these tools and these suggestions are con-
centrated and it’s very cool, because it is a lot of stuff,
technologies that you did not even know existed.
A large part of the users highlighted missing op-
tions to represent their event, in particular, to define
the types of events and audience types in more detail,
U1 exemplifies this with a particular event situation,
the audience remains very dispersed in the environ-
ment of the event. U4 already addresses a different
perspective, about considering a criterion to measure
the amount of users for the use of certain technology
in the event. This is an understandable point of view,
since some of the technologies presented are intended
for large crowds, while others are not.
In addition to the general perception about the util-
ity of the tool, it is important to analyze the ease of use
of the tool in relation to aspects, such as usability and
user experience. From Figure 2, it is noted that these
criteria are mostly positive, however the item PEU2
has variances that deserve attention.
This reflection must be made because many users
find it difficult to visualize the technologies, and this
item is reflected in the comments of the participants
during the study, as can be seen in the quotes: U2
“It’s a very long list. What do I do with so many op-
tions?”; U4 “I think the biggest problem was the
way you showed the possible technologies. I think
that long list doesn’t make it easy”. However, some
users did not have this same difficulty. U2, for exam-
ple, comments that it was super easy to use the tool
and added “in just over 1 minute, you already have
the possibilities that would be better for your event”.
It is noted that the main reason is a usability prob-
lem, and that this problem has affected the user ex-
perience in viewing the technologies, thus hinder-
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 2: Acceptance questionnaire result based on the TAM model.
ing their satisfaction. From this issue, users sug-
gested creating categories for technologies, or a fil-
ter where only technologies with compatibility above
70% would appear in the final results list.
On the other hand, it is noted that these results
did not affect the intention to use the tool, since the
attributes IU1 and IU2 received only positive marks,
highlighting the tool as an interesting approach for ex-
Finally, although the interface was a confusing cri-
terion from the point of view of some users, which
was not the objective, the performance of the tool
fulfills its role in recommending technologies, and is
represented by the comments and acceptance of the
tool using TAM. Among the improvements, the rec-
ommendation proposals need to be reviewed, as well
as the implementing and improving new criteria for
the relationship between events.
The results presented in this paper reflect the expe-
rience of experts regarding the acceptance of a tech-
nology recommendation platform for events. Given
this, a few necessary steps were followed in order to
ensure that the research is completed correctly. How-
ever, during the acceptance process, possible limita-
tions were identified.
The first reflects the current context of the
COVID-19 pandemic, since when this study was con-
ducted, event organizers spent a long period without
holding physical events and were limited to only vir-
tual events. This is also reflected in the application of
the study, which was applied entirely online.
Besides, another limitation of this study refers to
the low number of participants who evaluated the plat-
form (six in total). However, the six were not ordinary
users, all are specialists with experience in the pro-
duction/organization of events.
Another limitation can be observed in the under-
standing of users regarding the presentation of crite-
ria and problems in the interface, since some users
had doubts about certain criteria and subcriteria, and
highlighted problems in the description. To mitigate
this limitation, researchers were available to answer
any question from the users during the study.
In the research presented here, we propose a trian-
gulation of the classifications of technologies, events
and audience, which is implemented in a func-
tional prototype of a knowledge-based recommenda-
tion system, namely, Tech4Events.
The validation test was developed with six event
promoters. At this stage of the process, we realized
that the tool fulfills its proposed objective and that it
had great acceptance by the target audience, which
can be perceived in the data provided in Section 6. In
addition, it was also possible to realize that improve-
ments are still needed, with regard to the system in-
terface, since a significant portion of the test partici-
pants pointed out this factor as something that should
be improved. This will be the next stage of our re-
search, and will be carried out with the development
of an interaction model.
Despite the need, as predicted by the authors, for
an interaction model, the objective of the validation
It’s a Match! A Knowledge based Recommendation System for Matching Technology with Events
carried out in Phase (iii) was to verify that the rec-
ommendation met the demands of the organizers, a
mechanism that was validated. Additionally, the ex-
perts participating in the survey classified the pro-
posal as innovative and useful, thus revealing the po-
tential of Techs4Events as an innovative system.
From the tests, it was possible to plan future goals,
among them, improving the way the tool commu-
nicates with the end user and reorganizing the way
the software provides the final information. (list of
recommended technologies). Among the suggestions
from the participants, grouping the technologies by
common categories and improving the calculation of
the compatibility of technologies with the event are
high priority points in the list of future improvements.
Thus, Techs4Events was considered satisfactory,
achieving great acceptance from the users participat-
ing in the tests and proved to be functionally effective
and relevant.
This research, carried out within the scope of the
Samsung-UFAM Project for Education and Research
(SUPER), according to Article 48 of Decree no
6.008/2006(SUFRAMA), was funded by Samsung
Electronics of Amazonia Ltda., under the terms
of Federal Law no 8.387/1991, through agreement
001/2020, signed with Federal University of Ama-
zonas and FAEPI, Brazil and through agreement
003/2019 (PROPPGI), signed with ICOMP/UFAM.
Also supported by Coordination for the Improvement
of Higher Education Personnel - Brazil (CAPES) -
Financing Code 001, CNPq process 311494/2017-0,
and Foundation for Research Support of the State
of Amazonas (FAPEAM) - POSGRAD and process
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