Interactive recommendation of social network communication between cities based on GNN and user preferences
To further enhance the effectiveness of interactive recommendation for intercity social network communication, the study proposes a novel interactive recommendation model for intercity social network communication. This model focuses on point-of-interest preference migration and recommendation model...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-04-01
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| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2024-0068 |
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| Summary: | To further enhance the effectiveness of interactive recommendation for intercity social network communication, the study proposes a novel interactive recommendation model for intercity social network communication. This model focuses on point-of-interest preference migration and recommendation modeling by integrating the self-attention mechanism, cross-attention mechanism, and graph neural network. The method effectively solves the problem of cross-city users’ interest point preference change and constructs a recommendation framework based on interest point heat. The experimental results showed that the recommendation accuracy of the new model was up to 93.52%. Compared with the existing more advanced recommendation methods, the recommendation coverage of Chengdu–Chongqing dining and food points of interest could be up to 96.72%. The recommendation coverage of Shanghai–Beijing business and residential and science, education, and culture points of interest could be up to 95.17%, and the maximum time reduction was 2.07 s. The contribution of this study is the introduction of a novel graph attention mechanism, which improves the accuracy and stability of recommendations. This provides an effective technical solution for intercity social network communication recommendations. |
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| ISSN: | 2192-8029 |