Cited By
View all- Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
Similarity-based Multi-Relational Attention Network for Social Recommendation
Pages 307 - 317
Abstract
Graph Neural Networks have been widely used in the field of social recommendation systems. However, with the increase of diffusion depth, it tends to cause the over-smoothing problem, which inhibits its performance. In this work, we propose a similarity-based multi-relational attention network, called Similarity-based Multi-Relational Attention Network (SRAN), for social recommendation. Our model has three distinctive characteristics: (i) it tries to solve the data sparsity problem in social recommendation scenarios by using both user social relations and item homogeneous relations as supplementary information; (ii) it has an iteratively aggregating structure that mimics the structure of influence diffusion in user and item domain; (iii) it has two levels of attention mechanisms applied at the diffusion and aggregating level, enabling it to differentiate importance weights when constructing the user and item latent embeddings. Experiments conducted on two large-scale representative datasets demonstrate that the proposed algorithm outperforms previous methods substantially with 5.7% and 6.8% gains of HR@10 and NDCG@10 in both datasets against the state of the arts. Moreover, our SRAN alleviates the over-smoothing problem, and its performance can be further improved by increasing the diffusion depth.
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March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Published: 13 July 2022
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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View all- Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
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