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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10852203/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |