A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network
Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020029 |
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| _version_ | 1832544899783720960 |
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| author | Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao |
| author_facet | Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao |
| author_sort | Qiang Sun |
| collection | DOAJ |
| description | Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less accurate recommendation algorithm. The aim of this paper is to propose improvements on the standard substance diffusion algorithm, taking into account the influence of the user’s rating on the recommended item, adding a moderating factor, and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm. An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results. Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate. |
| format | Article |
| id | doaj-art-7b215583182f43d9b6e28d6b5d2673e7 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7b215583182f43d9b6e28d6b5d2673e72025-02-03T09:08:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017235737010.26599/BDMA.2023.9020029A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite NetworkQiang Sun0Leilei Shi1Lu Liu2Zixuan Han3Liang Jiang4Yan Wu5Yeling Zhao6School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UKSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Finance and Economics, Jiangsu University, Zhenjiang 212013, ChinaGrid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less accurate recommendation algorithm. The aim of this paper is to propose improvements on the standard substance diffusion algorithm, taking into account the influence of the user’s rating on the recommended item, adding a moderating factor, and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm. An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results. Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.https://www.sciopen.com/article/10.26599/BDMA.2023.9020029cloud computingbipartite graph networkrecommendation algorithmlink predictioncold start problem |
| spellingShingle | Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network Big Data Mining and Analytics cloud computing bipartite graph network recommendation algorithm link prediction cold start problem |
| title | A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network |
| title_full | A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network |
| title_fullStr | A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network |
| title_full_unstemmed | A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network |
| title_short | A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network |
| title_sort | novel recommendation algorithm integrates resource allocation and resource transfer in weighted bipartite network |
| topic | cloud computing bipartite graph network recommendation algorithm link prediction cold start problem |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020029 |
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