POI-based Recommender System for the Support of Academics in a
Smart Campus
Guilherme Xavier de Carvalho
1 a
, Isabela Gasparini
1 b
, Guilherme Medeiros Machado
2 c
Leandro Krug Wives
2 d
and Jos
e Palazzo M. de Oliveira
2 e
Santa Catarina State University (UDESC), Joinville, SC, Brazil
Informatic Institute - PPGCC, Federal University of RS (UFRGS), Porto Alegre, Brazil
Recommender Systems, Smart Campus, Points of Interest.
Recommender Systems are software used to suggest user items in a personalized and automated way. When
combined with Points of Interest (POI), they can set locations as referable items. This type of approach is
useful when the amount of POI available for the user is large. In the context of Universities, students have
different needs and have to look for different locations to experience the Universities’ resources. The goal
of this paper is to present a POI-based Recommender System to improve student’s well-being and to support
their academic journey in a Smart Campus. The recommender system was implemented by an application
called AONDE, which was used by 110 students, where 63 answered a satisfaction questionnaire allowing the
data collection needed for the the system evaluation. An accuracy of 61% in the recommendations of items to
students was measured, as well as a high satisfaction rate, where 90.5% of respondents said they were satisfied
or very satisfied with the locations suggested by the app. The purpose of this experience paper is demonstrate
that the approach here described proved to be useful for students’ routine, impacting positively their academic
Recommender systems (RS) were first proposed as a
solution to deal with the problem of user cognitive
overload, where the amount of information to be an-
alyzed exceeds the user capability (Machado et al.,
2018). RS is a subclass of information filtering sys-
tems that seeks to predict the “rating” or “preference”
a user would give to an item (Ricci et al., 2011). They
are used in a variety of applications, such as sugges-
tion of movies (e.g., Netflix), music (e.g., Spotify), or
videos (e.g., YouTube). When combined with Points
of Interest (POI), they can set locations as referable
items. This type of approach is useful when the
amount of POI available for the user is bigger than
the user capacity to analyze the full set. Therefore,
there is a need to customize recommendations to meet
the interests and needs of the user. Such systems can
be used in intelligent scenarios to improve the quality
of life of individuals in this environment; since data
filtering reduces the number of items associated with
users’ domain, their cognitive and informational load
are reduced.
The goal of this paper is to present a POI-based
Recommender System in a Smart Campus to sup-
port the academic journey in the environment. For
the system development, a literature review was per-
formed in search of related work and the basic con-
cepts were identified. The recommender system was
created, aligned primarily with the interests and needs
of its’ target audience. The algorithm implemented
represents the users and the items to be recommended
through a vector of tags, which are keywords that de-
scribe such an entity. Both users and items are struc-
tured in matrices and weighted by the metric of Term
Frequency–Inverse Document Frequency (TF-IDF).
Finally, the similarity between items and users is cal-
culated and generates the recommendations that are
presented through a web application, called AONDE,
which allows interaction with the user.
AONDE was used by 110 students of the Santa
Catarina State University - UDESC, and 63 answered
Xavier de Carvalho, G., Gasparini, I., Machado, G., Wives, L. and M. de Oliveira, J.
POI-based Recommender System for the Support of Academics in a Smart Campus.
DOI: 10.5220/0009514003980405
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 398-405
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
a satisfaction questionnaire and thus allowed the col-
lection of inputs used in the evaluation of the system.
Through these, an accuracy of 61% in the recommen-
dations of items to users was measured, as well as
a high satisfaction rate, where 90.5% of respondents
said they were satisfied or very satisfied with the lo-
cations suggested by the app.
In this section, we present the system that was de-
signed and developed to provide POI recommenda-
tions inside a smart campus. The strategy used in the
system is content-based since we did not have an an-
notated dataset (items previously evaluated). The sys-
tem is developed to attend two specific student stereo-
types, i.e., newcomers and veterans. More specifi-
cally, it is designed to help newcomers, i.e., students
that recently arrived at the campus and do not know
the environment, the routines, neither the resources
available to them. It is also able to help veteran stu-
dents to get involved with current activities and news,
since we found that they are usually more focused on
finishing their courses and do not have time to keep
up to date.
Based on these stereotypes, and also the user inter-
action, the system is able to identify the current user
needs and interests, classifying and prioritizing POI
accordingly to each user profile. Thus, not only the
most interesting items are recommended, but also the
ones most necessary to the academic life of each pro-
2.1 POI and User Modeling and
Both items to be recommended by the Recommender
System and user interests are represented within the
application through tags, keywords that define an el-
ement in a short, straightforward and clear manner.
Therefore, it was necessary to define the best tags
for each POI. A questionnaire was designed to col-
lect students’ perceptions of the locations found on
the university campus, as well as how they would de-
scribe them using tags.
The questionnaire was organized into three sec-
tions, which sought to generate mechanisms so that
we could understand, through user responses, some
aspects such as: which locations on campus were in-
teresting for students to know, with which tags certain
locations could be described, what was the profile of
freshman and senior student, what were the interests
of students as members of a college campus, what was
the relationship of the student’s course and the places
of interest.
Before being applied to the general public, a pilot
test was accomplished with students of the Human-
Computer Interaction discipline. This pilot test was
conducted to verify if the questionnaire was well
structured and organized and the interaction to an-
swer were analyzed. Such students contributed to the
refinement of the research instrument and once val-
idated, it was sent for access to all students on the
university through institutional emails and shares in
social networking groups of the institution. The ques-
tionnaire was available from August 6 to September
12, 2019; and 133 responses were collected. It is note-
worthy that there was at least one student response
from each of the 21 courses offered by the university,
among undergraduate and graduate students.
Based on information from this questionnaire
and three standard university documents the Fresh-
man Manual, the Extension Project Guide, and the
Pedagogical Curriculum Guidelines from UDESC,
we were able to define the representation of POIs,
courses, user interests and freshman stereotype using
a folksonomy.
According to Pereira e Silva (Pereira and da Silva,
2008) the folksonomy technique represents an initia-
tive to assist in the process of organization and at-
tribution of meaning to the contents available on the
web, as well as for the treatment of information over-
load. Thus, it is a content analysis and organization
approach based on the view of the interviewed audi-
ence and the author. The process of categorization of
information is based on the three pivots of folkson-
omy, the user - who performs the categorization; the
object - which is categorized; and the tags - which cat-
egorize this object. It is worth mentioning that this is
exactly the path to be followed through the question-
naire directed at the students.
Applying folksonomy can be understood as two
very distinct moments: information categorization
and information retrieval. In the first, users are given
a freedom of work, which enables them to generate
hypotheses and great information through a practical
and inexpensive process of both time and cognitive
effort. The second moment is related to the classifica-
tion of terms chosen during categorization, in short, is
to evaluate the quality of the answers and make deci-
sions from them. At this time freedom of work is on
the side of the experimenter, so it is possible to make
decisions by looking at the inputs collected from users
in the first phase of the approach and also from the
convictions that the project scope already has (Pereira
POI-based Recommender System for the Support of Academics in a Smart Campus
and da Silva, 2008).
The following sections explain how each of these
elements was elaborated, considering the glued sam-
pling and the applied folksonomy techniques, to be
implemented in the web application.
A survey had been carried out to define primarily
which POIs would be recommended by the system.
After data collection, it was possible to understand
which of those POIs were indeed relevant to the stu-
dents’ context. Such definitions emerged by applying
folksonomy techniques to the information base col-
lected and thus reached the number of 86 recommend-
able POIs within the UDESC Smart Campus.
Once the POIs were chosen, attention turned to
defining the tags that would describe a particular loca-
tion. At this time, the students’ answers were also an-
alyzed in order to understand their perceptions about
the content that would serve as a description of a par-
ticular POI. It was also through them that it was pos-
sible to extract the keywords for each of the recom-
mended places. However, it is important to note that
not all tags that make up the RS came directly from
the students’ verbalization or responses in the ques-
Having the folksonomy presented by Pereira and
Silva (2008) as the basis for this process of analysis
and definition, decisions were made looking at data
sampling based on the scope of the project. Thus,
some tags were elaborated through the interpretation
of the author of this work under the profile of the stu-
dents who participated in the research, relating the
academic journey of a student with the locations listed
as POI. We defined 83 distinct keywords, which were
scattered among the items to be recommended con-
textually, where each POI has at least 3 tags describ-
ing it.
Still on this process it is useful to emphasize that
the task of establishing the keywords for the POI
needs some care. Since if many POIs have a high tag
similarity rate, it will be difficult for the RS to differ-
entiate the specialties of each recommendation item.
Therefore, such a system may recommend items that
are not necessarily interesting to a user but which have
a high similarity index between the suggested tags.
On the other hand, if each POI has tags that are sig-
nificantly specific to it, it will be difficult to correlate
the items to be recommended with the user’s interests
more generally, thus requiring explicit and individu-
alized statements about the interest in each POI.
We have designed two scenarios to show the fea-
sibility of the system, one for each stereotype previ-
ously defined.
The first one considers a student named Aurora,
a freshman student at the university. She is informed
by the administration that a application is available
to help students get localized and to know the uni-
versity. She installs it on her smartphone and fills
in her profile and chooses some categories of inter-
est: Culture, entertainment and research. Right af-
ter, the system starts calculating the recommendations
and shows different POIs. Aurora starts her tour in
the campus, very excited, and starts sharing her expe-
rience with colleagues, which, in turn, start using the
application too. She finds out that some recommen-
dations, even if her colleagues have chosen the same
categories, are different (most of them are older than
her in the university). Hers interests are more related
to bureaucratic activities, needed in her initial stage.
The second time she used the application, the profile
was already filled and the recommendations were im-
mediate, but she was able to change her interests, per-
sonalizing the recommendations.
The second scenario considers a student named
Tobias, who is in the 3
year. He checked the bulletin
board and found the information about a new app.
Surprised by such news, he first wondered “When did
they create it?”. Even suspicious about what it was re-
ally about, he accessed the application. Tobias created
his profile by entering his personal details and select-
ing the categories that seemed convenient. A few mo-
ments later the system presented him with places that
would be of interest to him, respecting the newly cre-
ated profile. Analyzing the suggestions that appeared
on his list, Tobias realized that even though he was
a veteran, there were still several locations on cam-
pus he didn’t know about. The student was interested
in the functionality of the application, because it be-
came more updated with the actions that take place at
the university. Using other times, he noted that he can
modify his profile and disregard elements that are as-
sociated with activities he already knows, such as the
restaurant for example.
As our RS is content-based, the way in which the
content of the items is represented should be made
explicit. In this case, the items to be recommended
through the system are the POI. Therefore, these are
modeled through a structure where each POI is asso-
ciated with a set of keywords that describe that re-
spective POI, functioning as tags. An array of char-
acteristics (items x tags), where each line represents a
POI, so there will be a vector for each POI.
Considering these aspects and the feasibility of us-
ing tags to describe items, the second phase of the
content representation process deals with the weight-
ing of the items. According to Jannach et al. (Jannach
et al., 2010), the purpose of a content-based recom-
mender approach is not to maintain a list of items and
their static meta-information, but to categorize their
CSEDU 2020 - 12th International Conference on Computer Supported Education
relevant keywords in the form of weights. Therefore,
items are typically described using the TF-IDF en-
coding format (Term Frequency Inverse Document
Frequency) (Cantador et al., 2010) . Following the
approach via tags, where tags are used instead of doc-
uments, the TF-IDF encoding was adjusted and ac-
cording to the literature it can be applied in two ways:
in the first, only the Term Frequency (TF) is calcu-
lated, in the second the full scope of the formula is
taken into account. In this work, only the first version
of the formula is applied. Which describes an item
by means of tags, and precisely by using this artifice
there is no need to check the inverse frequency of a
term in the document (Cantador et al., 2010), after all
there are only a set of tags describing each item to be
recommended. Thus, the profile of an item is repre-
sented by the vector i
= (i
) expressed by the
equation below:
= t f
) (1)
Where t f
) corresponds to the number of times
(t f ) an item i
was highlight with the tag t
to a certain
item i
At the end of the weighting process, the set of
items will be represented as a vector of the weights
computed by the TF.
The representation of the user occurs in a similar
way to that of the item. In this scheme, user profile
is represented through a matrix of interest structure
(user x tags), where each line represents a user, so
there is a vector for each user. Each cell in the ma-
trix is linked to a specific tag and through this feature
it is possible to know which tags a user is associated
with. It is worth mentioning that these tags must be
collected through the user’s past interactions or ex-
plicitly asking the user when the user creates a profile
in the application.
Weights are also plotted in a vector space, adjusted
noting only by interest, but also by the user’s needs.
Therefore, the weighting of a user’s profile consists of
the vector u
= (u
), expressed by:
= t f
) (2)
Where t f
) corresponds to the number of times
(t f ) that user u
has been associated with tag t
a given user u
. After calculating the TF the user
vector proceeds to another weighting step, since still
in the user representation, it is necessary to consider
the recommendations for the freshman user profile.
For this purpose, a calibration in the value of the items
that make up the vector of interest of this type of user
is incorporated.
Knowing that the user is a freshman, it is pos-
sible to evaluate the interest vector of that user to
give more or less weight to certain interests, through
pre-processing.The evaluation in this stage considers
the stereotype of a freshman user. This stereotype is
a user vector F, which followed the representation
established for the user, but which already had pre-
defined tags according to the items (POI) mapped as
necessary for a freshman.
These POI considered necessary for freshmen
were taken from the University Freshman Manual,
which defines some main locations within the Univer-
sity campus. Therefore, this F stereotype is applied to
all users who use RS and are freshmen, that is, they
have been in the university for less than a year. It
is, therefore, a calibration imposed on the vector al-
ready calculated for these users. Such calibration is
performed by means of a simple average between the
vector of the user already weighted and the vector F,
in order to increase the weight of tags related to the
items defined as necessary for the freshmen. After
the user modeling process is finished, it is possible to
proceed to the recommendation algorithm, where the
similarity between item and user is calculated.
2.2 Finding Relevant POI
After the item and user were processed, the third stage
of processing deals with the similarity between the
item and user profiles. This is when the recommen-
dation is actually generated. For that, the cosine sim-
ilarity method, established as state of the art in this
context is applied (Ricci et al., 2015).
The proximity calculation can be performed using
the following equation, which is applied to the item
and users weighted vectors only by TF:
t f
) =
t f
) · t f
(t f
(t f
Where simCos
t f
) corresponds to the simi-
larity coefficient between the user vectors and items
(value belonging to the set [0.1]); t f
) is the num-
ber of times that the user u
has been associated with
textit tag t
; and t f
) deals with the number of times
that the item i
has been marked with the textit tag t
It is worth mentioning that for the calculation
of similarity, the corresponding mathematical expres-
sion was implemented in an algorithm in the Python
language, without the use of any specific library.
At the end of this step, the items to be recom-
mended to the user have already been defined. There-
fore, they are subject to suggestion and can be pre-
sented to the user.
POI-based Recommender System for the Support of Academics in a Smart Campus
2.3 Architecture of the System
With regard to architecture, the system is based on
the client-servers concept. A structure where service
providers, i.e. servers, are separated from those who
request the service, e.g. customers (Lee et al., 2004).
This relationship is conducted in such a way that cus-
tomers use server resources, without having direct ac-
cess to them. For that, it is on the basis of the ex-
change of requests that the application works.
The architecture is organized in three layers: pre-
sentation layer, business layer and data layer. This
model is implemented to organize the main entities
that make up the app. The presentation or interface
layer is the layer that interacts directly with the user.
Therefore, it is through it that requests for consulta-
tions are made and the results are displayed. The busi-
ness layer, on the other hand, is the logical part, where
the RS itself is, in order to relate to the other two lay-
ers for its full functioning. The third layer of this orga-
nization is the data layer. It is the entity composed of
the information repository that receives requests from
the business layer and executes these requests in the
The user receives recommendations through an
application. When accessing it, the user is interacting
in the presentation layer, without the need to know the
processes behind the RS itself. It is worth mentioning
that the application is on the client side, in the client-
server relationship, and relates to the server side when
sending and receiving data.
On the server side are the data and business layers.
Respectively, the first layer represents the database
with all the necessary information in the recommen-
dation process that is manipulated. The second, on the
other hand, stores the recommender’s business rules,
which is in fact the logical and coded definitions of
the RS. Once the architectural organization is estab-
lished, it is possible to specify the technical aspects
of the system in each of the layers. For the presen-
tation layer, a Web App was developed, a responsive
web application accessed from any browser installed
on computers or smartphones. Such a system was de-
veloped using HTML5, CSS3, JavaScript and PHP
The Web App communicates with the other layers
through RESTful API services developed in Python.
It is worth mentioning that the application design
process was guided by the Double Diamond model,
based on the Design Thinking approach. The data
layer of the system is structured by the PostgreSQL
As for the business layer, this is where the logical
part of the recommendation algorithm is located. RS
was implemented in Python and using the tools of the
language itself, a server was also built. Such business
layer resources were hosted in a structure that allows
users to access remotely. In this sense, the hosting
was performed on a server within the University’s in-
frastructure. Figures 1 and 2 present the recommen-
dation from AONDE application.
Figure 1: Recommendation of AONDE system in a desktop.
Figure 2: Recommendation of AONDE system in a smart-
For each recommended POI, the student can give
his/her feedback, selecting the “like” or “dislike” but-
tons presented just below the description of each POI.
The AONDE application was available to the Uni-
versity students and the experiment was carried out
between October 21 and 31, 2019. The release was
communicated through an institutional email, which
CSEDU 2020 - 12th International Conference on Computer Supported Education
would make it possible to reach a large part of the
students on the university campus.
During this current period of the experiment, 110
students registered in the application.The inputs gen-
erated from the interactions where they were mon-
itored periodically through the evaluation question-
naire and the database. After this period the access
to the application has been restricted and the data ma-
nipulation and analysis phase has started.
For the process of evaluating the AONDE appli-
cation and its recommender system, the metrics of
accuracy of the Recommender System (RS) and user
satisfaction regarding the use of the application were
established. So that such metrics could be analyzed,
two instruments were created. The first is related to
the formalism of precision and recall used to calcu-
late the accuracy of an RS, which is combined with
a feedback system (like and dislike) inserted in each
recommended POI. While the second is a question-
naire created to collect evidence related to the usabil-
ity of the system and user satisfaction.
The analysis of the data from this experimental
mechanism is based on the calculation of precision
and recall. In order to precision and recall to be calcu-
lated in a Recommender System, the top k items of
greatest relevance delivered by the system to the user
are used. In the context of our system, it was defined
that the 8 items with the highest recommended simi-
larity rate would represent this set. Precisely because
this is the number of POIs shown on the first page.
Therefore, the accuracy and recall were calculated for
each user who interacted with the system.
As an example, imagine that a student named Bob
accessed the system and received his recommenda-
tions after creating his profile. Bob interacts with the
system, navigating between pages, give “likes” and
“dislikes” in some of the suggested locations. This
interaction provides the necessary data to assess the
accuracy of the recommendations generated for Bob.
Suppose he liked 5 of the first 8 POIs presented, that
is, those on the first page, and that in total he liked
13 of all 86 recommended items, including those on
other pages. In this case, the precision would be
5/8 0.63, that is, 63%, where 5 corresponds to the
number of items defined as relevant selected by the
user, and 8 the total number of items considered rele-
vant.In this case the recall would result in the propor-
tion 5/13 0.38, that is, 38%, where 5 corresponds
to the number of items defined as relevant selected by
the user, and 13 the total number of items selected by
the user.
Applying this logic and analyzing the data col-
lected through the interactions of users with the items
recommended by AONDE, we can measure an accu-
racy of 61% and a recall equal to 66%, this having
limited to 8 the number of items considered relevant.
An accuracy of 61% represents that on average, for
every 10 items recommended, 6 met the expectations
of users, were in line with the profile and aligned with
the context. Therefore, it can be said that 61% of the
suggestions through AONDE were in fact assertive.
Thus, the probability that a selected item is the one
considered relevant for a student who interacted with
AONDE is 61%.
The RS recall was 66%, this represents the aver-
age number of times a relevant item is selected from
the total number of items selected. Thus, the greater
the recall, the greater the number of items considered
relevant to have been selected from the total. In this
context, with a 66% recall, it can be said that of all
items selected by the student during use, 66% were
among those considered relevant (top-8).
In short, the metrics collected through the data
samples point to a system with good recommendation
accuracy, considering the values of 61% for precision
and 66% for recall. The numbers indicate that in at
least more than 60% of the recommendations gener-
ated by the RS were relevant.
Of the 110 students who used AONDE applica-
tion and received recommendations, 63 students (i.e.
57%) answered the satisfaction questionnaire. Of
these students, 52.4% are male and 49.2% are fresh-
men. Students from 16 different courses participated
in the assessment (from 21 courses available in the
Regarding some usability issues from the ques-
tionnaire, they were presented in a 5-point likert scale.
Figure 3 presents the results. As Figure shows, most
students strongly agreed with the usability issues of
the app.
Regarding the question in which the student is in-
vited to say how useful for the academic journey of
a university student is to know the Points of Interest
(POI) recommended by the system, using a scale from
0 to 10, where 0 represents a lot useless and 10 very
useful, the Figure 4 presents the results. It can be seen
that the vast majority of academics agree that know-
ing such places is of great use, when voting 41.3% for
option 9 and 38.1% for option 10, totaling 50 of the
63 users who participated in the evaluation.
Students were also asked how satisfied they were
with the recommendations they received. As can be
seen in the Figure 5, the vast majority of those who
used the system and participated in the evaluation said
they were satisfied or very satisfied with the recom-
mendations received. Together these two groups rep-
resent 57 students, 90.5% of those who responded
to the evaluation. Finally, the students had the op-
POI-based Recommender System for the Support of Academics in a Smart Campus
Figure 3: Usability and User satisfaction with the application.
Figure 4: Utility of the system.
portunity to leave suggestions to AONDE improve-
ments. Among the collected ideas are to present the
geographical location of the POIs, as if in the form
of a map or using a GPS system in real time. In addi-
tion, several students showed interest in learning more
about POIs, so that each one had more details. An-
other aspect raised was the possibility of knowing the
value of similarity for each recommended item, in the
same way creating gamification strategies or integrat-
ing the application to social networks.
Recommendation systems (RS) are artifacts used to
manipulate large data sets, in order to reduce their
complexity by identifying and suggesting to users
only items of most interest or need. These software
tools seek to reduce the information burden while they
want to extract elements relevant to the context of use.
In the case of an environment integrated by an expres-
sive set of information, a Smart Campus proves to be a
suitable space for the application of an RS. Therefore,
an RS was proposed in the domain of the University,
whose objective is to support the students in their aca-
demic journey, relieving their cognitive and informa-
tional burden, with regard to the recommendation of
Points of Interest (POI) within the university campus.
After deepening and understanding about RS, the
proposal of this work was conceived, mainly aligned
with the vision of its target audience, since this project
is concerned with the use experience that it will offer.
Personas were drawn, representing the user’s profile
in the Smart Campus domain, resulting in a content-
based POI Recommender System, linked to a web ap-
plication called AONDE.
The proposed algorithm represents the users and
the items to be recommended through tags, that are
keywords that describe such an entity. In this way,
both users and items are structured in matrices and
weighted by the TF metrics.
An interesting point of the RS developed in this
work is its concern in recommending items that are
necessary for a student in his/her first steps at the uni-
versity, the freshman. A bias is applied to the user’s
profile, considering this stereotype, so that items fun-
damentally necessary for this type of student receive
a greater weight.
Finally, the similarity between items and user is
calculated, using the cosine similarity, and generate
the outputs for recommendation. During the devel-
CSEDU 2020 - 12th International Conference on Computer Supported Education
Figure 5: User satisfaction with the recommendations received.
opment phase, several tests were carried out in or-
der to align users’ expectations. In addition, a pilot
test was conducted before the system was released
to the public. University students were able to inter-
act with AONDE, personalize their profile and receive
POI recommendations within the campus. In the pe-
riod of the experiment, 110 users were created, which
enabled the present project to collect sufficient data
for analysis.
Having the accuracy of RS and user satisfac-
tion as metrics for evaluation, the RS was evaluated.
Through a feedback system present in each recom-
mended item and a satisfaction questionnaire, evi-
dence was collected that guarantees the benefits of the
system. It can be concluded that the system corre-
sponds to the desires of its target audience and actu-
ally contributes to the academic journey of a student
on campus, since 90.5% of the students say they felt
satisfied or very satisfied with the recommendations
received. Still on this evaluative perspective, the sys-
tem presented an accuracy in the recommendations
with an accuracy of 61% and a recall of 66%.
As future work the Recommender System can be
enhanced, since other recommender approaches could
be included in the system. For example, as more stu-
dents use the system on a regular basis, the collabora-
tive approach can be combined with content filtering.
This work offers the opportunity to connect several
other elements that may contribute to its dissemina-
tion and use in the university community, such as a
gamification system, whose goal would be to create
mechanisms of engagement and contribution to the
platform, disseminating knowledge and sharing expe-
riences in a playful and natural way.
The authors would like to thank the funding pro-
vided by FAPESC (public call FAPESC/CNPq No.
06/2016 support the infrastructure of CTI for young
researchers, project T.O. No.: 2017TR1755 - Am-
bientes Inteligentes Educacionais com Integrac¸
ao de
ecnicas de Learning Analytics e de Gamificac¸
01/2016 -
ao adaptativa para cidades inteligentes.
This study was financed in part by the Coordenac¸
de Aperfeic¸oamento de Pessoal de N
ıvel Superior -
Brasil (CAPES) Finance Code 001.
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POI-based Recommender System for the Support of Academics in a Smart Campus