Optimizing Academic Pairings in Smart Campuses: A Recommendation

System for Academic Communities

Elvandi da Silva Junior

1 a

, Gabriel Casanova

1 b

, Daniel Youssef de Hollanda Lopes

1 c

Ana Paula Militz Dorneles

1 d

, Renan Bordignon Poy

1 e

, Jos

e Palazzo M. de Oliveira

2 f

and Vin

ıcius Maran

1 g

Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Polytechnic School,

Federal University of Santa Maria, Av. Roraima, 1000, Santa Maria, Brazil

Informatics Institute, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Keywords:

Academic Pairings, Hybrid Filtering, Recommendation System, Smart Campus.

Abstract:

Collaborating across disciplines can advance research ﬁelds by offering new ways of addressing complex prob-

lems and fostering integration and acknowledging similarities among different ﬁelds. Pairing preferences, such

as teaching the same or different content areas, grade spans, or buildings, are also crucial in mentorship pro-

grams, as they can signiﬁcantly impact the effectiveness of the partnership. This necessary academic pairing

can be difﬁcult to a set of factors, as cultural, geographical or personal. This study addresses the challenge

of academic pairings, emphasizing the need to publicize university resources and projects to promote inter-

connection between professionals from different areas within the same academic environment. The research

describes the ”Uniﬁed Recommendation System” - a recommendation system for academic communities. This

is a hybrid recommendation system and was developed to recommend relevant projects of interest to the user,

in addition to being easy to access through an application for students, teachers and the general community.

Thus, the developed prototype demonstrated signiﬁcant potential as a relevant tool in the context of smart

campuses, with user interest recommendation rates of more than 81% in the evaluated scenario.

1 INTRODUCTION

Smart campuses are part of a broad category of smart

solutions designed to enhance human experiences in

a variety of ways, such as fostering connectivity, ef-

ﬁciency, and personalization (Sneesl et al., 2022b),

while also contributing to a more sustainable planet

(Clark and Eisenberg, 2008).

This category strongly emphasizes the integra-

tion of innovative tools into education (Huang et al.,

2019), striving to equip students, teachers and the

campus community with access to resources that hold

the potential to revolutionize the educational enviro-

ment into a more digitized and contemporary setting

https://orcid.org/0000-0001-8783-9578

https://orcid.org/0009-0009-5420-7334

https://orcid.org/0009-0006-4189-3812

https://orcid.org/0000-0002-8177-3763

https://orcid.org/0009-0004-7301-6609

https://orcid.org/0000-0002-9166-8801

https://orcid.org/0000-0003-1916-8893

(Yin, 2014).

One challenge that smart campuses educational

tools are well-equipped to address is Academic Pair-

ings. This involves a collaborative process where

academics and practitioners coexist in a shared

space, jointly generating knowledge (Stahl and Hesse,

2009). This process can also foster interprofessional

collaboration (Davoli and Fine, 2004), positively im-

pacting both the projects and the individuals involved

(Chiocchio et al., 2011; Karam et al., 2018; Green and

Johnson, 2015). Furthermore, these smart resources

can be useful for promoting ongoing university initia-

tives to the academic community.

Academic communities refer to groups of indi-

viduals who are involved in academic pursuits, such

as students, faculty, researchers, and administrators,

and who share common goals and interests related to

the pursuit, exchange, and validation of knowledge

(Wakeling et al., 2019). An effective strategy involves

the use of Recommendation Systems (RS), which are

commonly divided into the types: content-based ﬁl-

124

Silva Junior, E., Casanova, G., Lopes, D., Paula Militz Dorneles, A., Poy, R., M. de Oliveira, J. and Maran, V.

Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities.

DOI: 10.5220/0012630200003693

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 124-134

ISBN: 978-989-758-697-2; ISSN: 2184-5026

tering (CBF), collaborative ﬁltering (CF), and hybrid

ﬁltering (HF) (Adomavicius and Tuzhilin, 2005).

These systems have the capability to suggest items

to users (Aamir and Bhusry, 2015), including projects

and professors. In this context, this study aims to ad-

dress the problem of Academic Pairings at universi-

ties.

Thus, an intermediary–a recommendation sys-

tem must be established between users and poten-

tial educational items of interest, through an appli-

cation that employs a hybrid recommendation system

(Maruyama et al., 2023). To adress this, we developed

a recommendation system, considering the speciﬁcs

of educational items related to academic pairings. We

developed a prototype of this recommendation system

and evaluated it in the UFSM

university.

The paper is structured as follows: Section 2

presents a background on the subjects of Recom-

mender Systems and Smart Campuses; Section 3 pro-

vides an overview of related work; Section 4 de-

scribes the recommendation system built and its soft-

ware architecture; Section 5 discusses the data acqui-

sition and evaluation process in testing the recommen-

dation system and Section 6 summarizes the conclu-

sionss of this paper.

2 BACKGROUND

This section provides a comprehensive review of rec-

ommender systems, focusing on the most frequently

employed algorithms in recommendation systems.

Additionally, it explains the concept of smart cam-

puses, describes their various types, and different ap-

plication areas.

2.1 Recommender Systems

Recommender systems, which emerged in the 90s, are

ﬁltering tools designed to improve the quality of rec-

ommendations and are extensively utilized across the

internet (Batmaz et al., 2018). Currently, these sys-

tems are prevalent on various online platforms, such

as e-commerce, where they assist customers in lo-

cating products, and social networks,where they aid

users in identifying content of interest, as well as

other applications, such as banking.

These systems have become increasingly inte-

gral to users’ daily lives, due to their personalized

suggestions and their effectiveness surpassing that

of keyword-based search engines (Bai et al., 2020).

Whithin an academic context, recommender systems

https://www.ufsm.br

can prove to be exceptionally useful for a variety of

reasons. They function as facilitators of learning and

teaching tasks, enable students to make more appro-

priate and informed decisions, and assist organiza-

tions in gaining a better understanding of users (Ver-

bert et al., 2012; Hagemann et al., 2019; Zhou et al.,

2019).

Recommendation systems in a smart campus can

employ various techniques to provide relevant recom-

mendations to members of the academic community.

Below, some of these techniques and some of the al-

gorithms associated with each of them will be pre-

sented.

2.1.1 Content-Based Filtering

Content-Based Filtering utilizes detailed information

about academic items and user preferences to provide

relevant recommendations. ”Items” in the academic

context include courses, lectures, learning resources,

academic articles, events, and other elements related

to the academic environment. Content-Based Filter-

ing algorithms analyze the characteristics of the items

and create user proﬁles based on previously demon-

strated preferences (Banik, 2018). Among these algo-

rithms, we can mention:

• TF-IDF (Term Frequency-Inverse Document

Frequency). TF-IDF is a widely used algorithm

for text analysis. It assesses the importance of

keywords in documents based on how frequently

they appear relative to a set of documents. In the

academic context, TF-IDF can be applied to rec-

ommend study materials, academic articles, and

resources based on keywords and topics related

to the user’s interests. The algorithm ranks items

based on how well their characteristics (e.g., key-

words) match the user’s proﬁle (Karabiber, 2020).

• Word Embedding. Word embedding algorithms,

such as Word2Vec and GloVe, map words to real-

number vectors. These vectors represent the se-

mantic meaning of words and, by extension, aca-

demic items. In the academic context, these vec-

tors can be used to calculate semantic similarity

between items and suggest those with similar se-

mantic content to the user’s interests (Kenyon-

Dean et al., 2020).

2.1.2 Collaborative Filtering

Collaborative ﬁltering is based on the interactions and

behaviors of members of the academic community to

make recommendations. It considers how students,

professors, researchers, and other users interact with

each other, participate in academic activities, and col-

Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities

125

Figure 1: The framework of collaborative ﬁltering-based

RS (Chen et al., 2018).

laborate on research projects (Banik, 2018) (Figure

1).

This method can be user-based or item-based, as

described below:

• User-Based Collaborative Filtering: In this

method, user similarity is calculated based on

their past behaviors and preferences. The k-

Nearest Neighbors (KNN) algorithm is commonly

used to ﬁnd similar users. Once similar users are

identiﬁed, the system recommends items based on

the choices of these similar users that have not yet

been evaluated by the current user (Boehmke and

Greenwell, 2019).

• Item-Based Collaborative Filtering: In this ap-

proach, item similarity is calculated based on

user preferences. The KNN algorithm is an ex-

ample, and it calculates the similarity between

items based on user ratings. Then, it recommends

items similar to those that a user has already liked

(Boehmke and Greenwell, 2019).

The rationale of user-based CF and item-based CF

is shown in Figure 2.

Figure 2: The rationale of user-based CF and item-based

CF (Chen et al., 2018).

2.1.3 Hybrid Filtering

Hybrid ﬁltering combines elements of content-based

ﬁltering and collaborative ﬁltering to provide more

accurate and diverse recommendations. This ap-

proach can be executed in various ways, including in-

tegrating results from both approaches or switching

between them based on speciﬁc criteria. The algo-

rithms used in hybrid ﬁltering can include any combi-

nation of content-based and collaborative ﬁltering al-

gorithms, depending on the adopted strategy (Banik,

2018).

There are some techniques and models that can be

employed to achieve better results in hybrid ﬁltering.

Below are some of the most common types of hybrid

recommendations (Kharsa and Al Aghbari, 2023):

• Weighted Models: In hybrid systems, the results

of content-based ﬁltering and collaborative ﬁlter-

ing techniques are combined using a weighted

model. This model assigns weights to recommen-

dations generated by each technique based on user

context and preferences. The weighted combina-

tion of results helps provide balanced and person-

alized recommendations.

• Technique Switching: In this approach, the sys-

tem alternates between content-based ﬁltering and

collaborative ﬁltering based on user needs and

contextual characteristics. For example, it may

use collaborative ﬁltering to recommend study

groups based on users’ past interactions and then

apply content-based ﬁltering to suggest study

materials related to the topics discussed in the

groups.

• Recommendation-Level Machine Learning:

Machine learning algorithms, such as neural net-

works, can be used to automatically learn how

to combine the results of different ﬁltering tech-

niques in a hybrid recommendation model. This

enables dynamic adaptation to user preferences

and interaction patterns, resulting in highly per-

sonalized recommendations (Kharsa and Al Agh-

bari, 2023).

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126

Considering that the proposal of this work is cat-

egorized as Recommendation-Level Machine Learn-

ing, in the next sections we will present important

concepts that were used in developing the proposal:

Cosine Similarity and the KNN algorithm.

2.1.4 Cosine Similarity

Cosine similarity plays an important role in collab-

orative ﬁltering and content-based recommendation,

helping to identify similar users or items to create per-

sonalized recommendations (Chen et al., 2018). It can

be applied both to measure similarity between users

and between items in recommendation systems:

• Cosine Similarity for User: In this case, each

user is represented as a vector of item ratings.

Cosine similarity is calculated between the rating

vectors of two users to measure how similar their

likes and preferences are. High cosine similarity

between two users indicates that they have sim-

ilar preferences, which can be used to make rec-

ommendations based on the interactions of similar

users.

• Cosine Similarity for Item: In this case, each

item is represented as a vector of user ratings.

Cosine similarity is calculated between the rating

vectors of two items to measure how similar the

items are in terms of user preferences. High co-

sine similarity between two items indicates that

they are similar in terms of how they are rated by

users, which can be used to suggest similar items

when a user interacts with a speciﬁc item.

2.1.5 KNN Algorithm

The k-Nearest Neighbors (KNN) algorithm is a super-

vised machine learning method used for classiﬁcation

and regression (Khoa et al., 2013). It operates based

on the idea that similar objects tend to be close to each

other in a feature space (Zhang et al., 2018). KNN is

a simple and intuitive approach (Boehmke and Green-

well, 2019):

• Training. During the training phase, the algo-

rithm stores all examples from the training dataset

in a multi-dimensional space, where each example

is represented by a feature vector. Each example

is also associated with a class or target value.

• Classiﬁcation/Regression. When making predic-

tions for a new example, KNN searches for the k

nearest examples in the feature space. Proxim-

ity is typically measured using a metric like Eu-

clidean distance. The k nearest examples either

vote for the class of the example or contribute to

calculating a weighted average for regression.

• Choosing k. The choice of the k value is crucial

in KNN. A small k value (e.g., 1) makes the algo-

rithm sensitive to noise and outliers, while a large

k value smoothens the decision boundary, making

it less sensitive to local data variations.

• Weight Function. To address different weights

for nearest neighbors, you can assign weights

based on distance. Closer neighbors may have

larger weights than farther ones.

• Classiﬁcation vs. Regression. KNN can be used

for both classiﬁcation and regression tasks. In

classiﬁcation, the example is assigned to the most

frequent class among the k nearest neighbors. In

regression, a weighted average of the target values

of the k nearest neighbors is calculated.

• Performance. KNN’s performance can be inﬂu-

enced by the appropriate choice of distance met-

ric, data preprocessing, and selecting an optimal k

value. It’s also important to have a representative

training dataset.

2.1.6 Cold Start

The Cold start in recommendation systems refers to

situations in which the system needs to make rec-

ommendations without relevant historical data. This

challenge is particularly common when dealing with

new users or items and requires the application of cre-

ative strategies to provide a useful experience to users,

even when information is limited.

User-related cold start occurs when a new user

registers in the recommendation system and hasn’t

had signiﬁcant interactions yet. Given that the system

lacks information about this user’s preferences, mak-

ing personalized recommendations becomes a chal-

lenge. On the other hand, item-related cold start hap-

pens when a new item is introduced into the recom-

mendation system and lacks a history of interactions

with users. In this scenario, the system has no data on

how users have reacted to that speciﬁc item (Joy et al.,

2021).

2.2 Smart Campuses

In the realm of technological advancements, a con-

cept that has gained signiﬁcant prominence is that of

a Smart Campus. It can be characterized as an entity

that strategically employs technology and infrastruc-

ture with the main goal of improving its processes,

thus improving their utility for individuals (S

anchez-

Torres et al., 2018).

The realization of this concept can be achieved

through the deployment of a diverse array of tools.

These include IoT (Internet of Things) technology,

Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities

127

sensors, cloud computing, user interfaces, blockchain

and a variaty of other applications (Gilman et al.,

2020; Fern

andez-Caram

es and Fraga-Lamas, 2019).

Each of these components can facilitate the creation

and implementation of Smart Campus, contributing

unique beneﬁts to the system.

Some of the beneﬁts include the enhanced qual-

ity of life for inhabitants of these environments; en-

hanced user experience for students, staff, and es-

tate managers, optimized space utilization, improved

management of resources such as computer labs and

air conditioners and energy conservation, among oth-

ers (Villegas-Ch. et al., 2019; Sutjarittham et al.,

2018; Wang, 2014).

In spite of these beneﬁts, smart campuses also face

signiﬁcant challenges. These include limitations of

perspectives that rely entirely on data, the integration

of individuals’ practices and the importance of con-

sidering ethics and domestication (Bates and Friday,

2017). Additional challenges involve the absence of a

technology adoption model, perceived fees, perceived

trust and perceived value (Sneesl et al., 2022a).

3 RELATED WORK

This section provides an overview of related research

in recommender systems and smart campuses, high-

lighting recommended item types and ﬁltering tech-

niques employed in each work.

(Ibrahim et al., 2019) introduced a framework tar-

geting the academic domain, recommending various

courses for students. Their hybrid system combines

collaborative and content-based ﬁltering with ontol-

ogy for information extraction.

(Bai et al., 2019) explored a collaborative recom-

mendation system for researchers with similar inter-

ests. The model incorporates collaborative ﬁltering,

content-based ﬁltering, and social network-based ﬁl-

tering, forming a hybrid approach.

(Mrhar and Abik, 2019) proposed a recommenda-

tion system for online course platforms, offering per-

sonalized suggestions based on user proﬁles. The au-

thor utilizes content-based ﬁltering and deep learning

to enhance algorithm accuracy.

(Xiao et al., 2018) presents a personalized recom-

mendation system that tailors suggestions to individ-

ual users based on their interests and learning history.

The system employs both content-based and collabo-

rative ﬁltering techniques to recommend didactic ma-

terials and essential learning resources.

This work aims to develop a platform that recom-

mends a wide variety of items based on previous stud-

ies. Table 1 summarizes the types of recommended

resources and the methods used for processing data.

The proposed system adapts precisely to individ-

ual user preferences. It offers a personalized and

proﬁle-based recommendation experience that accu-

rately aligns with user-deﬁned preferences during

item acquisition. This is achieved through the inte-

gration of advanced algorithms and recommendation

elements, named as ”Uniﬁed Recommendation Sys-

tem”.

4 A RECOMMENDATION

SYSTEM FOR ACADEMIC

COMMUNITIES

The “Uniﬁed Recommendation System” serves as a

software architecture for smart campuses, offering a

variety of recommender systems and techniques tai-

lored to different item types.

4.1 Data Aquisition

Obtaining data is a crucial step in optimizing recom-

mendation systems, providing the essential founda-

tion for ﬁltering algorithms to analyze a wide range

of information. In the world of recommendation sys-

tems, data is the lifeblood that yields valuable insights

into user preferences, behaviors, and trends. This pro-

cess entails not only the collection of diverse data but

also ensuring that recommendation algorithms have a

robust basis for generating accurate and pertinent sug-

gestions.

A Python script was used to extract data from the

UFSM projects and professors system, throught a web

scrapping process. In this way, a total of 56865 items

were obtained, divided between projects, professors

and events.

From the data obtained, it was necessary to to an-

alyze the collected resources, separating them into

different categories and topics, so that the ﬁlter-

ing algorithm could obtain greater precision in the

search and recommendation. To accomplish this task,

we used the ChatGPT 3.5 Large Language Model

(LMM)(OpenAI, 2023) to categorize items based on

a series of frequently used generic categories. This

step resulted in 9540 categories and subcategories.

4.2 Software Architecture Deﬁnition

The software architecture to support recommenda-

tion processes is segmented into three main modules,

which is as shown in Figure 3: The User Access Mod-

ule (UAM), the Recommendation Management mod-

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Table 1: Studies and Characteristics of Recommendation Systems.

Work Type of Recommended Item Filtering Strategy

(Ibrahim et al., 2019) Undergraduate and post-

graduate courses

Ontology-based ﬁltering, Content-based ﬁlter-

ing, Collaborative ﬁltering

(Bai et al., 2019) Collaborating researchers Collaborative ﬁltering, Content-based ﬁltering,

Social network-based ﬁltering

(Mrhar and

Abik, 2019)

Online courses Content-based ﬁltering, Deep Learning

(Xiao et al., 2018) Didactic materials for learning Collaborative ﬁltering, Content-based ﬁltering

(Hoang et al., 2022) Courses in specialized ﬁelds Ontology-based ﬁltering, Content-based ﬁlter-

ing, Collaborative ﬁltering

This research Educational resources (courses,

mini-courses, video lessons,

teaching materials, e-books,

lectures, events, scientiﬁc articles,

thesis, similar user proﬁles,

other educational platforms)

Collaborative ﬁltering, Content-based ﬁltering

ule (RMM), and the Administrative and Persistence

Module (APM).

The modules of the software architecture are pre-

sented below:

4.2.1 User Access Module (UAM)

In the user access module, users engage with the ma-

terial provided through recommendations, by using

their personal devices such as cell phones, comput-

ers, and alternative devices. This section allows users

to interact with resources, direct themselves to other

university portals, and communicate their interests.

The module is composed by three submodules:

(i) User Interface Module, responsible for presenting

recommendations appropriately to the user, (ii) Con-

trol Module: Responsible for capturing user usage in-

formation and sending usage statistics to the recom-

mendation and administration modules, and (iii) Per-

sistence and Communication module, responsible for

storing information collected in the recommendation

process and make requests to other modules of the ar-

chitecture (using the REST standard).

UAM was prototyped using the Ionic framework

(Yusuf, 2016), which allows the generation of web

and mobile applications.

4.2.2 Administrative and Persistence Module

(APM)

The Administrative and Persistence Module is the re-

sponsible for the communication with university ex-

ternal systems. It is also responsible to manage and

store user proﬁle and navigation information.

The module is composed by four submodules:

(i) Communication module, responsible for make re-

quests to other modules of the architecture (using the

REST standard), (ii) Persistence module, responsible

for the storage of the data collected in the recom-

mendation process, and to retrieve items to present

to users in a recommendation, (iii) User Navigation

Module, responsible to store and retrieve user data

given by the UAM module, and (iv) User Proﬁle Pro-

cessig Module, responsible for the management of

user proﬁle data and his/her topics of interest.

APM was prototyped using Java SpringBoot

framework (Yusuf, 2016) using JDBC driver to the

communication with PostgreSQL database. APM

stores resource descriptions, user interests, interacted

items, and recommendation history. All recommen-

dations are saved in this section and, when requested,

sent to the recommendation management module to

initiate the ﬁltering and recommendation process.

4.2.3 Recommendation Management Module

(RMM)

The recommendation management module contains

all the code blocks and functions of the recommen-

dation system. Here, new codes are added, edited and

corrected, building the core of the personalized rec-

ommendation process for each user. This environ-

ment processes user information, sends requests of

the database, and operates as an indirect approach, re-

ﬂecting the user’s actions and interactions.

One of the key strengths of the platform’s recom-

mendation system lies in its utilization of a hybrid ap-

proach, seamlessly blending both content-based and

collaborative ﬁltering techniques.

By incorporating these two distinct ﬁltering meth-

ods, the platform optimizes the diversity and fresh-

ness of recommendations. Content-based ﬁltering al-

lows the system to understand the intrinsic character-

Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities

129

Figure 3: “Uniﬁed Recommendation System” software architecture deﬁnition.

istics of educational resources and match them with

the user’s preferences. On the other hand, collabora-

tive ﬁltering leverages collective user behavior to sug-

gest resources that align with the preferences of users

with similar tastes. Changing between these ﬁlters

guarantees that whenever a user asks for recommen-

dations, they receive new suggestions.

The algorithm encompassing Content-Based Fil-

tering adheres to a sequence of operations for gener-

ating recommendations. The ﬁrst step begins by cat-

aloging the user’s interests into tables, which are sub-

sequently associated with the user’s topics. Following

this, educational resources that align with these topics

are then listed based on the the user-topic relationship.

Each resource undergoes a process known as ‘bag-

of-words’, which isolates keywords, quantiﬁes their

occurrences, and compiles a list accordingly. An anal-

ogous process is conducted for user-favorite items.

Cosine similarity is then employed to calculate the

similarity between text features. Ultimately, the fea-

tures bearing the highest similarity are recommended

to the user and and can be seen later displayed on the

screen.

Having users previously declared their interests,

the algorithm employs Collaborative Filtering by

seeking out other users with comparable interests,

creating a list for each. The interests of both users

are combined, with a numerical increment for each

shared interest. The algorithm then selects the recom-

mended resources for the users with the highest sim-

ilarity and compiles them into a list. This list is then

shufﬂed and ultimately presented to the user.

RMM was prototyped in Python language, with

Scikit (Kramer and Kramer, 2016) and Surprise (Hug,

2020) libraries.

5 EVALUATION

5.1 Evaluation Scenario

In order to assess the accuracy of resource recom-

mendations, a proﬁle representing a student from the

Polytechnic School of UFSM who has a broad inter-

est in the ﬁeld of technology has been created, which

is as shown in Figure 4. This proﬁle was created with

the intention of delving deeper into the analysis of the

effectiveness of recommendations provided to users,

considering their preferences and afﬁnities with the

technology ﬁeld as a whole.

In this situation, topics of interest such as Tech-

nology, Applied Technology, Assistive Technology,

Automotive Technology, Banking Technology, BIM

Technology, and Bluetooth Technology (as shown in

Figure 5a) were selected. This approach enabled the

system to search for relevant data and information re-

lated to these speciﬁc subjects and to identify other

users who shared the same interests. Thus, a dy-

namic platform was created that promotes intercon-

nection among individuals with diverse technology-

related afﬁnities, enriching the user experience and

fostering knowledge exchange and collaboration.

The evaluation of each recommended resource oc-

curs by sliding it to the side of the screen, which is as

shown in Figure 5b, where the left represents disin-

terest and the right signiﬁes that the user wants more

recommendations of that type. This simple and intu-

itive interaction provides users with effective control

over the suggested content, allowing them to further

customize their experience according to their prefer-

ences. As users interact with the system, they provide

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130

Figure 4: New user registration screen.

valuable feedback that continually enhances the ac-

curacy of recommendations, making the learning and

discovery environment even more tailored to their in-

dividual needs.

5.2 Results and Discussion

In the proposed test scenario, the recommendation

system returned a sample consisting of 58 recom-

mended items in about 2 seconds, as shown in Figure

6. Among these items, we noticed that 47 of them

piqued the user’s interest, while 11 did not generate

interest, resulting in an accuracy rate of 81% in rec-

ommendations related to this speciﬁc proﬁle.

The accuracy of the algorithm tends to improve as

users interact more with the system and accumulate a

more substantial data history. This enhancement be-

comes even more noticeable as we add more users

and data to our database. This growth results in an

increase in the number of users with similar interests,

which, in turn, leads to more reﬁned recommenda-

tions aligned with each user’s individual preferences.

The recommendation process, based on user pref-

erences and interests, has proven to be an effective

tool for presenting projects aligned with their selected

topics of interest. The recommendations have high-

lighted projects directly related to speciﬁc areas of

interest, as well as professors in those ﬁelds or who

were directly involved in a project.

The presence of projects that piqued the interest of

users with similar proﬁles added an additional layer of

relevance, suggesting that the recommendations not

only met individual criteria but also reﬂected com-

mon trends and preferences within the community of

users with similar interests. Furthermore, the identi-

ﬁcation of professors with expertise and involvement

in the areas of interest enriched the recommendations

by providing a direct connection to the academic and

practical knowledge of these professionals.

However, it is important to note that projects with

similar keywords were recommended, even if they

did not align with the user’s speciﬁc interest. This

occurred, for example, when projects related to Lan-

guage Technologies or Methodologies for Social Sci-

ences were suggested due to the broad search for

”Technology,” even if the user had not explicitly se-

lected these categories.

The similarity in keywords between the recom-

mended resources and the selected categories can also

lead to confusion during the evaluation process. Mis-

interpretation of an item due to shared project titles

or similar elements can result in inaccurate assess-

ments. This inaccuracy in evaluation can, in turn, lead

to errors in recommendations, underscoring the im-

portance of enhancing recommendation accuracy to

avoid misunderstandings and optimize the user expe-

rience.

As users continue to use the system and provide

more feedback, the algorithm becomes more proﬁ-

cient in understanding and anticipating their needs

and preferences. This process initiates an ongoing

cycle of improvement, making the system progres-

sively more effective in suggesting relevant and perti-

nent content.

Furthermore, as our user community grows, and

more people share similar opinions and interests, the

algorithm also beneﬁts from collective wisdom. This

further contributes to the increased accuracy of rec-

ommendations. Therefore, the continuous enhance-

ment of the algorithm’s accuracy is a dynamic process

that beneﬁts all users, enriching their experience and

making it more personalized.

6 CONCLUSION

The implementation of innovative technologies has

brought signiﬁcant modernization to the ﬁeld of e-

learning, especially in smart academic environments.

This has resulted in the creation of a new scenario

Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities

131

(a). Interface to select topics of inter-

est by the user (in Portuguese).

(b). Recommendation evaluation screen – the user can swipe to right or left,

see more details about the item or mark it as favorite.

Figure 5: Example of interfaces provided by the mobile application.

Figure 6: Interest statistics screen.

where communication among various mobile devices

plays a fundamental role.

This study highlights the effectiveness of a rec-

ommendation system that is based on individual user

interests to address the challenges that teachers, stu-

dents, and the academic community as a whole face

in ﬁnding compatible academic peers and facilitating

connections within the academic community. How-

ever, it also recognizes the importance of improving

the accuracy of recommendations in order to avoid

potential inaccuracies, enhance the user experience,

and increase the efﬁciency of the proposed solution.

One innovative feature of this study is its approach

to connecting the academic community with projects

aligned with their interests, revealing opportunities

that might otherwise have gone unnoticed. The most

notable aspect is that the recommendation process

uses multiple techniques to recommend items to aca-

demic communities.

However, it is important to note that this study has

limitations, such as the evaluation process being con-

ducted at a single university, the absence of testing

with a large number of users, and the lack of longi-

tudinal data to track changes in user interests over

time. Possible areas for future research include ex-

panding testing to other universities, enhancing sys-

tem accuracy, incorporating Deep Learning and Neu-

ral Network techniques, conducting performance tests

CSEDU 2024 - 16th International Conference on Computer Supported Education

132

on different hardware and platforms, adding georef-

erencing features for points of interest in the applica-

tion, and utilizing ontologies for even more personal-

ized recommendations.

ACKNOWLEDGEMENTS

This research is supported by CNPq/MCTI/FNDCT

n. 18/2021 grant n. 405973/2021-7 and CNPq/MCTI

Nº 10/2023 - UNIVERSAL grant n. 402086/2023-6.

The research by Jos

e Palazzo M. de Oliveira is par-

tially supported by CNPq grant 306695/2022-7 PQ-

SR. The research by Vin

ıcius Maran is partially sup-

ported by CNPq grant 306356/2020-1 DT-2, CNPq

PIBIC and PIBITI program and FAPERGS PROBIC

program.

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