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,

Tha

ıs Castro and Bruno Gadelha

Institute of Computing, Federal University of Amazonas, Manaus, AM, Brazil

Keywords:

Recommendation Systems, Interaction Technology, Events Planning.

Abstract:

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.

1 INTRODUCTION

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

reports

. These technologies were classiﬁed based on

their own characteristics combined with the charac-

teristics of the events where they can be used (Gomes

https://doi.org/10.6084/m9.ﬁgshare.14050388.v2

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

525

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.

2 BACKGROUND

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

public.

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

“Grafﬁto”.

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.,

2011).

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

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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.

3 METHODOLOGY

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-

making.

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.

4 TECHS4EVENTS: EVENT

TECHNOLOGY

RECOMMENDER

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

527

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-

nologies.

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-

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Table 1: Example of how the recommendation is made.

Criterion

Subcriterion selected

by the user

Subcriterion that

technology has

Score

Venue Open Open 1

Network

infrastructure

availability

Not required Not required 1

Audience

disposition

Fixed Fixed, Random 1

Audience behavior

manifestation

Active Passive 0

Audience interaction Planned Planned 1

Type of event

Cultural

celebration

Arts and

entertainment,

Cultural celebration

Type of audience

Mass,

community,

incidental

Mass,

community,

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

percentage.

5 EVALUATION OF Tech4Events

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,

2000).

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

529

6 RESULTS AND DISCUSSION

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

experience

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

years

Participant 3 46 - 50 M

Arts and entertainment

Educational and scientiﬁc events

Private events

Recreational events

more than 10

years

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

Techs4Events.

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-

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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-

perts.

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.

7 LIMITATIONS AND THREATS

TO VALIDITY

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.

8 FINAL CONSIDERATIONS

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

531

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.

ACKNOWLEDGMENT

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 nº

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

062.00150/2020.

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