SVD-gorank: Recommender system algorithm using SVD and gower's ranking
Recommender systems using ranking-oriented collaborative filtering are currently widely used. One widely used approach is a memory-based model with ranking orientation. Recently, a ranking algorithm that combines user rating values from SVD (Singular Value Decomposition) and user similarity values h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43836/ http://umpir.ump.edu.my/id/eprint/43836/1/SVD-gorank_Recommender%20system%20algorithm%20using%20SVD%20and%20gower%27s%20ranking.pdf |
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| author | Saifudin, Ilham Widiyaningtyas, Triyanna Zaeni, Ilham Ari Elbaith Aminuddin, Afrig |
| author_facet | Saifudin, Ilham Widiyaningtyas, Triyanna Zaeni, Ilham Ari Elbaith Aminuddin, Afrig |
| author_sort | Saifudin, Ilham |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recommender systems using ranking-oriented collaborative filtering are currently widely used. One widely used approach is a memory-based model with ranking orientation. Recently, a ranking algorithm that combines user rating values from SVD (Singular Value Decomposition) and user similarity values has been proposed. The problem is that this algorithm is limited to only the rating weights used. This results in an accuracy value that can still be improved. Therefore, this research proposes a new collaborative filtering-based algorithm that combines the matrix factorisation method using SVD and the ranking method by utilising Gower's Coefficient similarity weight as an aggregation component known as the SVD-GoRank method. Experimental results using the MovieLens-100K, MovieLens-1M, Book-Crossing, Ciao, Epinions, Flixster, and MovieLens-10M datasets can provide the best accuracy results at the Top-N level, especially in the NDCG, MRR, Precision, Hit Rate, and Recall metrics, which are indicators important in recommendation systems that focus on the relevance of recommendations at the top of the list. Apart from that, the SVD-GoRank algorithm can also have efficient running time. |
| first_indexed | 2025-11-15T03:53:17Z |
| format | Article |
| id | ump-43836 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:17Z |
| publishDate | 2025 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-438362025-02-17T08:04:21Z http://umpir.ump.edu.my/id/eprint/43836/ SVD-gorank: Recommender system algorithm using SVD and gower's ranking Saifudin, Ilham Widiyaningtyas, Triyanna Zaeni, Ilham Ari Elbaith Aminuddin, Afrig QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Recommender systems using ranking-oriented collaborative filtering are currently widely used. One widely used approach is a memory-based model with ranking orientation. Recently, a ranking algorithm that combines user rating values from SVD (Singular Value Decomposition) and user similarity values has been proposed. The problem is that this algorithm is limited to only the rating weights used. This results in an accuracy value that can still be improved. Therefore, this research proposes a new collaborative filtering-based algorithm that combines the matrix factorisation method using SVD and the ranking method by utilising Gower's Coefficient similarity weight as an aggregation component known as the SVD-GoRank method. Experimental results using the MovieLens-100K, MovieLens-1M, Book-Crossing, Ciao, Epinions, Flixster, and MovieLens-10M datasets can provide the best accuracy results at the Top-N level, especially in the NDCG, MRR, Precision, Hit Rate, and Recall metrics, which are indicators important in recommendation systems that focus on the relevance of recommendations at the top of the list. Apart from that, the SVD-GoRank algorithm can also have efficient running time. Institute of Electrical and Electronics Engineers Inc. 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43836/1/SVD-gorank_Recommender%20system%20algorithm%20using%20SVD%20and%20gower%27s%20ranking.pdf Saifudin, Ilham and Widiyaningtyas, Triyanna and Zaeni, Ilham Ari Elbaith and Aminuddin, Afrig (2025) SVD-gorank: Recommender system algorithm using SVD and gower's ranking. IEEE Access, 13. pp. 19796-19827. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2025.3533558 https://doi.org/10.1109/ACCESS.2025.3533558 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Saifudin, Ilham Widiyaningtyas, Triyanna Zaeni, Ilham Ari Elbaith Aminuddin, Afrig SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title | SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title_full | SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title_fullStr | SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title_full_unstemmed | SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title_short | SVD-gorank: Recommender system algorithm using SVD and gower's ranking |
| title_sort | svd-gorank: recommender system algorithm using svd and gower's ranking |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/43836/ http://umpir.ump.edu.my/id/eprint/43836/ http://umpir.ump.edu.my/id/eprint/43836/ http://umpir.ump.edu.my/id/eprint/43836/1/SVD-gorank_Recommender%20system%20algorithm%20using%20SVD%20and%20gower%27s%20ranking.pdf |