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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10852203/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575571440173056 |
|---|---|
| author | Ilham Saifudin Triyanna Widiyaningtyas Ilham Ari Elbaith Zaeni Afrig Aminuddin |
| author_facet | Ilham Saifudin Triyanna Widiyaningtyas Ilham Ari Elbaith Zaeni Afrig Aminuddin |
| author_sort | Ilham Saifudin |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-258ad3b0d603427394dd3b5f49ae66fc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-258ad3b0d603427394dd3b5f49ae66fc2025-01-31T23:04:38ZengIEEEIEEE Access2169-35362025-01-0113197961982710.1109/ACCESS.2025.353355810852203SVD-GoRank: Recommender System Algorithm Using SVD and Gower’s RankingIlham Saifudin0https://orcid.org/0000-0002-2063-4524Triyanna Widiyaningtyas1https://orcid.org/0000-0001-6104-6692Ilham Ari Elbaith Zaeni2https://orcid.org/0000-0001-9665-8613Afrig Aminuddin3https://orcid.org/0000-0003-0963-5419Department of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Malang, IndonesiaDepartment of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Malang, IndonesiaDepartment of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Malang, IndonesiaDepartment of Computer Graphic and Multimedia, Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, MalaysiaRecommender 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.https://ieeexplore.ieee.org/document/10852203/Collaborative filteringrecommender systemsingular value decompositionGower’s ranking |
| spellingShingle | Ilham Saifudin Triyanna Widiyaningtyas Ilham Ari Elbaith Zaeni Afrig Aminuddin SVD-GoRank: Recommender System Algorithm Using SVD and Gower’s Ranking IEEE Access Collaborative filtering recommender system singular value decomposition 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 x2019 s ranking |
| topic | Collaborative filtering recommender system singular value decomposition Gower’s ranking |
| url | https://ieeexplore.ieee.org/document/10852203/ |
| work_keys_str_mv | AT ilhamsaifudin svdgorankrecommendersystemalgorithmusingsvdandgowerx2019sranking AT triyannawidiyaningtyas svdgorankrecommendersystemalgorithmusingsvdandgowerx2019sranking AT ilhamarielbaithzaeni svdgorankrecommendersystemalgorithmusingsvdandgowerx2019sranking AT afrigaminuddin svdgorankrecommendersystemalgorithmusingsvdandgowerx2019sranking |