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A Novel Recommendation of Learning Items Based on Deep Neural Networks and Trust Relationships

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Bibliographic Details
Title: A Novel Recommendation of Learning Items Based on Deep Neural Networks and Trust Relationships
Language: English
Authors: Yamina Aissaoui, Lamia Berkani, Faiçal Azouaou
Source: International Journal of Learning Technology. 2024 19(4):397-421.
Availability: Inderscience Publishers. World Trade Centre Building II 29 route de Pre-Bois Case Postale 856 CH-1215, Geneva 15, Switzerland. e-mail: editor@inderscience.com; Web site: https://www.inderscience.com/jhome.php?jcode=ijlc
Peer Reviewed: Y
Page Count: 25
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Information Networks, Online Searching, Search Strategies, User Needs (Information), Social Networks, Interest Inventories, Profiles, Open Educational Resources, Trust (Psychology), Electronic Learning, Information Retrieval
DOI: 10.1504/IJLT.2024.143969
ISSN: 1477-8386
1741-8119
Abstract: With the rapid development of information technologies, online learning platforms have become the most convenient way for users(teachers and learners) to share their learning content. However, due to the increasing number of educational content, it becomes very difficult for learners to find the most appropriate items. Most previous methods focused on users' ratings to establish learner's profiles, while recent work has added users' comments. In this work we are interested on the usage of social learning networks and we propose a novel learning items recommendation approach through sentiment analysis. Two different deep neural network models (DNNs) have been used, namely: bidirectional encoder representations from transformers (BERT) and recurrent neural networks (RNNs) with their modified version, long short-term memory (LSTM). These models are based on the learners' data, including their favourite content and comments. To support learners in selecting learning resources, a list of trusted learners was developed using similarity between learners. In order to evaluate the effectiveness of our proposal, experiments have been conducted on two different datasets. The results we have obtained demonstrated that our approach outperforms the baselines and related work.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1458116
Database: ERIC
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  Data: A Novel Recommendation of Learning Items Based on Deep Neural Networks and Trust Relationships
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  Data: <searchLink fieldCode="AR" term="%22Yamina+Aissaoui%22">Yamina Aissaoui</searchLink><br /><searchLink fieldCode="AR" term="%22Lamia+Berkani%22">Lamia Berkani</searchLink><br /><searchLink fieldCode="AR" term="%22Faiçal+Azouaou%22">Faiçal Azouaou</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Learning+Technology%22"><i>International Journal of Learning Technology</i></searchLink>. 2024 19(4):397-421.
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  Data: Inderscience Publishers. World Trade Centre Building II 29 route de Pre-Bois Case Postale 856 CH-1215, Geneva 15, Switzerland. e-mail: editor@inderscience.com; Web site: https://www.inderscience.com/jhome.php?jcode=ijlc
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  Data: With the rapid development of information technologies, online learning platforms have become the most convenient way for users(teachers and learners) to share their learning content. However, due to the increasing number of educational content, it becomes very difficult for learners to find the most appropriate items. Most previous methods focused on users' ratings to establish learner's profiles, while recent work has added users' comments. In this work we are interested on the usage of social learning networks and we propose a novel learning items recommendation approach through sentiment analysis. Two different deep neural network models (DNNs) have been used, namely: bidirectional encoder representations from transformers (BERT) and recurrent neural networks (RNNs) with their modified version, long short-term memory (LSTM). These models are based on the learners' data, including their favourite content and comments. To support learners in selecting learning resources, a list of trusted learners was developed using similarity between learners. In order to evaluate the effectiveness of our proposal, experiments have been conducted on two different datasets. The results we have obtained demonstrated that our approach outperforms the baselines and related work.
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        Value: 10.1504/IJLT.2024.143969
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      – Text: English
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      Pagination:
        PageCount: 25
        StartPage: 397
    Subjects:
      – SubjectFull: Information Networks
        Type: general
      – SubjectFull: Online Searching
        Type: general
      – SubjectFull: Search Strategies
        Type: general
      – SubjectFull: User Needs (Information)
        Type: general
      – SubjectFull: Social Networks
        Type: general
      – SubjectFull: Interest Inventories
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      – SubjectFull: Profiles
        Type: general
      – SubjectFull: Open Educational Resources
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      – SubjectFull: Trust (Psychology)
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      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Information Retrieval
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      – TitleFull: A Novel Recommendation of Learning Items Based on Deep Neural Networks and Trust Relationships
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            NameFull: Faiçal Azouaou
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              Type: published
              Y: 2024
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