A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach
Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such ap...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2023-10-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | http://www.etasr.com/index.php/ETASR/article/view/6325 |
| _version_ | 1797660036133027840 |
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| author | Aadil Alshammari Mohammed Alshammari |
| author_facet | Aadil Alshammari Mohammed Alshammari |
| author_sort | Aadil Alshammari |
| collection | DOAJ |
| description | Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.
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| first_indexed | 2024-03-11T18:24:55Z |
| format | Article |
| id | doaj.art-4c58a3deccd647049769366ac70c9647 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-11T18:24:55Z |
| publishDate | 2023-10-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-4c58a3deccd647049769366ac70c96472023-10-14T05:46:58ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-10-0113510.48084/etasr.6325A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic ApproachAadil Alshammari0Mohammed Alshammari1Information Systems Department, Faculty of Computing and Information Technology, Northern Border University, Saudi ArabiaComputer Science Department, Faculty of Computing and Information Technology, Northern Border University, Saudi ArabiaExisting recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information. http://www.etasr.com/index.php/ETASR/article/view/6325artificial intelligencemachine learningrecommender systemsprobabilistic modelssocial media |
| spellingShingle | Aadil Alshammari Mohammed Alshammari A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach Engineering, Technology & Applied Science Research artificial intelligence machine learning recommender systems probabilistic models social media |
| title | A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach |
| title_full | A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach |
| title_fullStr | A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach |
| title_full_unstemmed | A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach |
| title_short | A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach |
| title_sort | recommendation engine model for giant social media platforms using a probabilistic approach |
| topic | artificial intelligence machine learning recommender systems probabilistic models social media |
| url | http://www.etasr.com/index.php/ETASR/article/view/6325 |
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