Recommendation Method Based on Factor Decomposition Machine and Graph Neural Network
This paper introduces a recommendation model combining factorization machines and graph neural networks.By recursively propagating cross-features among neighboring nodes and utilizing attention mechanisms,and use cross feature relationship domains to obtain attention weights.Experimental results validate its effectiveness,offering valuable insights for future research.
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