A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering
Social media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical cluste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00088.pdf |
| _version_ | 1797213228382552064 |
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| author | Yousif Hawas Abbas Adel Al-Shaher Mohamed |
| author_facet | Yousif Hawas Abbas Adel Al-Shaher Mohamed |
| author_sort | Yousif Hawas Abbas |
| collection | DOAJ |
| description | Social media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical clustering. The study uses a database that includes user posts, comments, likes, shared content, and user profiles. The DSOM algorithm analyzes and organizes the data, while hierarchical clustering enhances performance. By utilizing the insights gathered from this social content database, we can significantly improve the accuracy and relevance of recommendations. This improvement will ultimately increase user engagement and satisfaction on social media platforms. The findings of this research have implications for recommendation systems on social media platforms and strategies related to promoting content and analyzing user behavior. |
| first_indexed | 2024-04-24T10:54:56Z |
| format | Article |
| id | doaj.art-0c7fc11dd29349a29f6c79342e5c88fd |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:54:56Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-0c7fc11dd29349a29f6c79342e5c88fd2024-04-12T07:36:22ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970008810.1051/bioconf/20249700088bioconf_iscku2024_00088A novel approach to social content recommendation using deep self-organizing maps and hierarchical clusteringYousif Hawas Abbas0Adel Al-Shaher Mohamed1Al-Ayen Iraqi University, Computer Engineering TechniquesCollege of Computer Science and Mathematics, University of Thi-QarSocial media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical clustering. The study uses a database that includes user posts, comments, likes, shared content, and user profiles. The DSOM algorithm analyzes and organizes the data, while hierarchical clustering enhances performance. By utilizing the insights gathered from this social content database, we can significantly improve the accuracy and relevance of recommendations. This improvement will ultimately increase user engagement and satisfaction on social media platforms. The findings of this research have implications for recommendation systems on social media platforms and strategies related to promoting content and analyzing user behavior.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00088.pdf |
| spellingShingle | Yousif Hawas Abbas Adel Al-Shaher Mohamed A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering BIO Web of Conferences |
| title | A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering |
| title_full | A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering |
| title_fullStr | A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering |
| title_full_unstemmed | A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering |
| title_short | A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering |
| title_sort | novel approach to social content recommendation using deep self organizing maps and hierarchical clustering |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00088.pdf |
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