Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the poten...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/1898 |
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| author | Yaowei Duan Liang Zhang Xu Lu Junqing Li |
| author_facet | Yaowei Duan Liang Zhang Xu Lu Junqing Li |
| author_sort | Yaowei Duan |
| collection | DOAJ |
| description | With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). This algorithm first optimizes the embeddings of users and items using their respective feature matrix, then learns the latent embedding representations of users and items through a lightweight graph convolutional network. Finally, the latent embedding representations are incorporated as key parameters into the hybrid spreading recommendation algorithm to generate recommendations. Comparative experiments on two publicly available datasets, MovieLens and Douban, demonstrate that LGCNHS achieves improved accuracy and diversity in recommendations compared to related methods. The algorithm code is available on github. |
| format | Article |
| id | doaj-art-336b1cdd80d04577b43788fd3523a598 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-336b1cdd80d04577b43788fd3523a5982025-08-20T03:11:20ZengMDPI AGApplied Sciences2076-34172025-02-01154189810.3390/app15041898Light Graph Convolutional Recommendation Algorithm Based on Hybrid SpreadingYaowei Duan0Liang Zhang1Xu Lu2Junqing Li3College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Taian 271018, ChinaWith the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). This algorithm first optimizes the embeddings of users and items using their respective feature matrix, then learns the latent embedding representations of users and items through a lightweight graph convolutional network. Finally, the latent embedding representations are incorporated as key parameters into the hybrid spreading recommendation algorithm to generate recommendations. Comparative experiments on two publicly available datasets, MovieLens and Douban, demonstrate that LGCNHS achieves improved accuracy and diversity in recommendations compared to related methods. The algorithm code is available on github.https://www.mdpi.com/2076-3417/15/4/1898recommendation systemspreading-based recommendationgraph convolutional network |
| spellingShingle | Yaowei Duan Liang Zhang Xu Lu Junqing Li Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading Applied Sciences recommendation system spreading-based recommendation graph convolutional network |
| title | Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading |
| title_full | Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading |
| title_fullStr | Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading |
| title_full_unstemmed | Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading |
| title_short | Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading |
| title_sort | light graph convolutional recommendation algorithm based on hybrid spreading |
| topic | recommendation system spreading-based recommendation graph convolutional network |
| url | https://www.mdpi.com/2076-3417/15/4/1898 |
| work_keys_str_mv | AT yaoweiduan lightgraphconvolutionalrecommendationalgorithmbasedonhybridspreading AT liangzhang lightgraphconvolutionalrecommendationalgorithmbasedonhybridspreading AT xulu lightgraphconvolutionalrecommendationalgorithmbasedonhybridspreading AT junqingli lightgraphconvolutionalrecommendationalgorithmbasedonhybridspreading |