Structural centrality of networks can improve the diffusion-based recommendation algorithm
The recommendation system has become an indispensable information technology in the real world. The recommendation system based on the diffusion model has been widely used because of its simplicity, scalability, interpretability, and many other advantages. However, the traditional diffusion-based re...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.1018781/full |
| _version_ | 1798030405011505152 |
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| author | Yixiu Kong Yizhong Hu Xinyu Zhang Cheng Wang |
| author_facet | Yixiu Kong Yizhong Hu Xinyu Zhang Cheng Wang |
| author_sort | Yixiu Kong |
| collection | DOAJ |
| description | The recommendation system has become an indispensable information technology in the real world. The recommendation system based on the diffusion model has been widely used because of its simplicity, scalability, interpretability, and many other advantages. However, the traditional diffusion-based recommendation model only uses the nearest neighbor information, which limits its efficiency and performance. Therefore, in this article, we introduce the centralities of complex networks into the diffusion-based recommendation system and test its performance. The results show that the overall performance of heat conduction algorithm can be improved by 184%–280%, using the centrality of complex networks, reaching almost the same accuracy level as the mass diffusion algorithm. Therefore, the recommendation system combining the high-order network structure information is a potentially promising research direction in the future. |
| first_indexed | 2024-04-11T19:40:52Z |
| format | Article |
| id | doaj.art-a00755e1bb60477c8b1c244b5d1ab977 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-11T19:40:52Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-a00755e1bb60477c8b1c244b5d1ab9772022-12-22T04:06:43ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-10-011010.3389/fphy.2022.10187811018781Structural centrality of networks can improve the diffusion-based recommendation algorithmYixiu Kong0Yizhong Hu1Xinyu Zhang2Cheng Wang3School of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaTsinghua Education Foundation, Tsinghua University, Beijing, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaThe recommendation system has become an indispensable information technology in the real world. The recommendation system based on the diffusion model has been widely used because of its simplicity, scalability, interpretability, and many other advantages. However, the traditional diffusion-based recommendation model only uses the nearest neighbor information, which limits its efficiency and performance. Therefore, in this article, we introduce the centralities of complex networks into the diffusion-based recommendation system and test its performance. The results show that the overall performance of heat conduction algorithm can be improved by 184%–280%, using the centrality of complex networks, reaching almost the same accuracy level as the mass diffusion algorithm. Therefore, the recommendation system combining the high-order network structure information is a potentially promising research direction in the future.https://www.frontiersin.org/articles/10.3389/fphy.2022.1018781/fullrecommendation systemcentralitycomplex networkdiffusion modelcollaborative filtering |
| spellingShingle | Yixiu Kong Yizhong Hu Xinyu Zhang Cheng Wang Structural centrality of networks can improve the diffusion-based recommendation algorithm Frontiers in Physics recommendation system centrality complex network diffusion model collaborative filtering |
| title | Structural centrality of networks can improve the diffusion-based recommendation algorithm |
| title_full | Structural centrality of networks can improve the diffusion-based recommendation algorithm |
| title_fullStr | Structural centrality of networks can improve the diffusion-based recommendation algorithm |
| title_full_unstemmed | Structural centrality of networks can improve the diffusion-based recommendation algorithm |
| title_short | Structural centrality of networks can improve the diffusion-based recommendation algorithm |
| title_sort | structural centrality of networks can improve the diffusion based recommendation algorithm |
| topic | recommendation system centrality complex network diffusion model collaborative filtering |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.1018781/full |
| work_keys_str_mv | AT yixiukong structuralcentralityofnetworkscanimprovethediffusionbasedrecommendationalgorithm AT yizhonghu structuralcentralityofnetworkscanimprovethediffusionbasedrecommendationalgorithm AT xinyuzhang structuralcentralityofnetworkscanimprovethediffusionbasedrecommendationalgorithm AT chengwang structuralcentralityofnetworkscanimprovethediffusionbasedrecommendationalgorithm |