BROAD-RSI – educational recommender system using social networks interactions and linked data

Abstract There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate p...

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Main Authors: Crystiam Kelle Pereira, Fernanda Campos, Victor Ströele, José Maria N. David, Regina Braga
Format: Article
Language:English
Published: SpringerOpen 2018-03-01
Series:Journal of Internet Services and Applications
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13174-018-0076-5
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spelling doaj-04fd31c3ecc24a568c4ab12714e7d1372020-11-25T00:33:26ZengSpringerOpenJournal of Internet Services and Applications1867-48281869-02382018-03-019112810.1186/s13174-018-0076-5BROAD-RSI – educational recommender system using social networks interactions and linked dataCrystiam Kelle Pereira0Fernanda Campos1Victor Ströele2José Maria N. David3Regina Braga4Postgraduate Program in Informatic, Federal University of the State of Rio de Janeiro (UNIRIO)Computer Science Postgraduate Program, Knowledge Engeneering Research Group, Federal University of Juiz de Fora (UFJF)Computer Science Postgraduate Program, Knowledge Engeneering Research Group, Federal University of Juiz de Fora (UFJF)Computer Science Postgraduate Program, Knowledge Engeneering Research Group, Federal University of Juiz de Fora (UFJF)Computer Science Postgraduate Program, Knowledge Engeneering Research Group, Federal University of Juiz de Fora (UFJF)Abstract There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.http://link.springer.com/article/10.1186/s13174-018-0076-5Educational recommender systemSocial networkLinked data
collection DOAJ
language English
format Article
sources DOAJ
author Crystiam Kelle Pereira
Fernanda Campos
Victor Ströele
José Maria N. David
Regina Braga
spellingShingle Crystiam Kelle Pereira
Fernanda Campos
Victor Ströele
José Maria N. David
Regina Braga
BROAD-RSI – educational recommender system using social networks interactions and linked data
Journal of Internet Services and Applications
Educational recommender system
Social network
Linked data
author_facet Crystiam Kelle Pereira
Fernanda Campos
Victor Ströele
José Maria N. David
Regina Braga
author_sort Crystiam Kelle Pereira
title BROAD-RSI – educational recommender system using social networks interactions and linked data
title_short BROAD-RSI – educational recommender system using social networks interactions and linked data
title_full BROAD-RSI – educational recommender system using social networks interactions and linked data
title_fullStr BROAD-RSI – educational recommender system using social networks interactions and linked data
title_full_unstemmed BROAD-RSI – educational recommender system using social networks interactions and linked data
title_sort broad-rsi – educational recommender system using social networks interactions and linked data
publisher SpringerOpen
series Journal of Internet Services and Applications
issn 1867-4828
1869-0238
publishDate 2018-03-01
description Abstract There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.
topic Educational recommender system
Social network
Linked data
url http://link.springer.com/article/10.1186/s13174-018-0076-5
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