Interactive recommendation of social network communication between cities based on GNN and user preferences
Year: 2025
Type: article
Abstract: 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.
Source: Nonlinear Engineering
Authors: Yanbo Gao, Lin Zhang
Institutions: Soochow University, Suzhou University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,187
OpenAlex ID: W4409528411