Self-Supervised Social Recommendation Algorithm Fusing Residual Networks
Social recommendation based on graph neural networks learns the embedded relationships between users and items through the information of social graphs and interaction graphs to get the final recommendation results. However, the existing algorithms mainly utilize the static social graph structure, w...
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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-12-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2401006.pdf |
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| Summary: | Social recommendation based on graph neural networks learns the embedded relationships between users and items through the information of social graphs and interaction graphs to get the final recommendation results. However, the existing algorithms mainly utilize the static social graph structure, which is unable to mine the potential linking relationship between users, and at the same time do not solve the noise problem in the user-item interaction behavior. Therefore, a self-supervised social recommendation algorithm incorporating residual networks is proposed. Firstly, the algorithm employs a variational hypergraph auto-encoder for link prediction in social networks to obtain a reconstructed social graph, which is used to mine the positive link relationships hidden among users. Secondly, an attention mechanism is utilized to assign different attention coefficients to the original and the reconstructed residual social graphs to obtain a more accurate representation of users. Lastly, to alleviate the problem of noise in the data, an adaptive hypergraph global relation extractor is constructed. Self-supervised signals are created using local embedding information and global embedding information in collaboration with this extractor, which optimizes the local embedding representation and thus mitigates the effect of noise. The algorithm is experimentally compared with baseline models such as NGCF, LightGCN, and MHCN on three datasets, Ciao, Epinions and Yelp. On the Ciao dataset, Recall@10 is improved by 17.1% to 48.5%, NDCG@10 is improved by 1.4% to 37.9%; on the Epinions dataset, Recall@10 is improved by 8.3% to 56.2%, NDCG@10 is improved by 3.7% to 29.8%; on the Yelp dataset, Recall@10 is improved by 9.1% to 53.3%, NDCG@10 is improved by 11.2% to 66.6%. Experimental results show that the algorithm has good recommendation performance compared with the benchmark model. |
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| ISSN: | 1673-9418 |