A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad
Social support is essential, especially in a working environment, because it can reduce psychological strain. The psychological strain associated with mental health in daily lives could have been gained from mismatched staffing and indirect control of the staffs. In order to meditate the problem, in...
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| Format: | Article |
| Language: | en |
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Universiti Teknologi MARA
2020
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| Online Access: | https://ir.uitm.edu.my/id/eprint/48122/1/48122.pdf https://ir.uitm.edu.my/id/eprint/48122/ https://mjoc.uitm.edu.my |
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| _version_ | 1833068575576817664 |
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| author | Zolkafly, Ahmad Subhi Ahmad, Rahayu |
| author_facet | Zolkafly, Ahmad Subhi Ahmad, Rahayu |
| author_sort | Zolkafly, Ahmad Subhi |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Social support is essential, especially in a working environment, because it can reduce psychological strain. The psychological strain associated with mental health in daily lives could have been gained from mismatched staffing and indirect control of the staffs. In order to meditate the problem, in this study, a hybrid serendipity social recommender model is proposed. This model is a combination of several proposed models encountered during the development process. Firstly, Rough Set Theory (RST) has been used in the early development stage to compute an automated attribute selection. RST is a mathematical tool that is widely used for knowledge discovery and feature selection. At the same time, it minimizes redundancies among variables in classifying objects and extracts rules from the database. In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. This algorithm aims to define the higher matching suggested attributes and used for processing massive data. Lastly, a reasoning approach is applied by using case-based reasoning from the result produced. The reasoning approach is used to finding the reasons why the attributes are selected. This approach searches the history of the selected attributes or compute a reason by digging back in the database |
| format | Article |
| id | my.uitm.ir-48122 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2020 |
| publisher | Universiti Teknologi MARA |
| record_format | eprints |
| spelling | my.uitm.ir-481222021-06-24T09:44:25Z https://ir.uitm.edu.my/id/eprint/48122/ A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad mjoc Zolkafly, Ahmad Subhi Ahmad, Rahayu Multimedia systems Computer software Data mining Social support is essential, especially in a working environment, because it can reduce psychological strain. The psychological strain associated with mental health in daily lives could have been gained from mismatched staffing and indirect control of the staffs. In order to meditate the problem, in this study, a hybrid serendipity social recommender model is proposed. This model is a combination of several proposed models encountered during the development process. Firstly, Rough Set Theory (RST) has been used in the early development stage to compute an automated attribute selection. RST is a mathematical tool that is widely used for knowledge discovery and feature selection. At the same time, it minimizes redundancies among variables in classifying objects and extracts rules from the database. In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. This algorithm aims to define the higher matching suggested attributes and used for processing massive data. Lastly, a reasoning approach is applied by using case-based reasoning from the result produced. The reasoning approach is used to finding the reasons why the attributes are selected. This approach searches the history of the selected attributes or compute a reason by digging back in the database Universiti Teknologi MARA 2020-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/48122/1/48122.pdf A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad. (2020) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 5 (2). pp. 563-571. ISSN 2600-8238 https://mjoc.uitm.edu.my |
| spellingShingle | Multimedia systems Computer software Data mining Zolkafly, Ahmad Subhi Ahmad, Rahayu A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title | A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title_full | A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title_fullStr | A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title_full_unstemmed | A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title_short | A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad |
| title_sort | hybrid serendipity social recommender model / ahmad subhi zolkafly and rahayu ahmad |
| topic | Multimedia systems Computer software Data mining |
| url | https://ir.uitm.edu.my/id/eprint/48122/1/48122.pdf https://ir.uitm.edu.my/id/eprint/48122/ https://mjoc.uitm.edu.my |
| url_provider | http://ir.uitm.edu.my/ |