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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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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