Contrastive Dynamic Graph for Sequential Recommendation
To alleviate the problems in dynamic graph sequential recommendation,such as sparse and noisy user-item interaction da-ta,and the requirement for a large number of labels,this paper proposes a new dynamic graph sequential recommendation method based on contrastive learning,which is called CDGSR(Contrastive Dynamic Graph for Sequential Recommendation).Specifically,CDGSR designed three different contrastive learning methods from coarse-grained to fine-grained:inter layer contrastive learning,twice propagation contrastive learning and random noise perturbation contrastive learning.The experimental results demonstrate that CDGSR achieves NDCG@10 scores of 0.363 3,0.587 3,and 0.522 0 on the real-world datasets of Amazon-Beauty,Amazon-Games,and Amazon-CDs,respectively.Additionally,the corresponding Hit@10 scores are 0.525 8,0.778 6,and 0.735 9.Compared to ma-trix factorization-based methods like BPR-MF and FPMC,neural network-based methods like GRU4Rec,Caser,SASRec,and graph neural network-based methods like SR-GNN,HGN,HyperRec,and DGSR,CDGSR consistently achieves the best results.Specifi-cally,compared to the best-performing method DGSR,CDGSR improves NDCG@10 by 1.97%and Hit@10 by 1.60%on the Ama-zon-CDs dataset.These results indicate that CDGSR can effectively utilize contrastive learning to improve the performance of dy-namic graph sequential recommendation method.
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