Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR)
One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/6/4/121 |
| _version_ | 1797461409847574528 |
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| author | Triyanna Widiyaningtyas Muhammad Iqbal Ardiansyah Teguh Bharata Adji |
| author_facet | Triyanna Widiyaningtyas Muhammad Iqbal Ardiansyah Teguh Bharata Adji |
| author_sort | Triyanna Widiyaningtyas |
| collection | DOAJ |
| description | One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However, this algorithm suffers in the execution time with an increased number of items. Therefore, this study proposes a new recommendation algorithm that combines the matrix decomposition method and ranking aggregation to reduce the time complexity. The matrix decomposition method utilizes singular decomposition value (SVD) to predict the unrated items. The ranking aggregation method applies weight point rank (WPR) to obtain the recommended items. The experimental results with the MovieLens 100K dataset result in a faster running time of 13.502 s. In addition, the normalized discounted cumulative gain (NDCG) score increased by 27.11% compared to the WP-Rank algorithm. |
| first_indexed | 2024-03-09T17:19:58Z |
| format | Article |
| id | doaj.art-39d22463fd774158bd7a7377f048f19d |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-09T17:19:58Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-39d22463fd774158bd7a7377f048f19d2023-11-24T13:17:39ZengMDPI AGBig Data and Cognitive Computing2504-22892022-10-016412110.3390/bdcc6040121Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR)Triyanna Widiyaningtyas0Muhammad Iqbal Ardiansyah1Teguh Bharata Adji2Department of Electrical Engineering, Universitas Negeri Malang, Malang 65145, IndonesiaWidya Analytic Company, Yogyakarta 55291, IndonesiaDepartment of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaOne of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However, this algorithm suffers in the execution time with an increased number of items. Therefore, this study proposes a new recommendation algorithm that combines the matrix decomposition method and ranking aggregation to reduce the time complexity. The matrix decomposition method utilizes singular decomposition value (SVD) to predict the unrated items. The ranking aggregation method applies weight point rank (WPR) to obtain the recommended items. The experimental results with the MovieLens 100K dataset result in a faster running time of 13.502 s. In addition, the normalized discounted cumulative gain (NDCG) score increased by 27.11% compared to the WP-Rank algorithm.https://www.mdpi.com/2504-2289/6/4/121collaborative filteringranking aggregationSVDWPR |
| spellingShingle | Triyanna Widiyaningtyas Muhammad Iqbal Ardiansyah Teguh Bharata Adji Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) Big Data and Cognitive Computing collaborative filtering ranking aggregation SVD WPR |
| title | Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) |
| title_full | Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) |
| title_fullStr | Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) |
| title_full_unstemmed | Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) |
| title_short | Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR) |
| title_sort | recommendation algorithm using svd and weight point rank svd wpr |
| topic | collaborative filtering ranking aggregation SVD WPR |
| url | https://www.mdpi.com/2504-2289/6/4/121 |
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