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M2GRec: An efficient sequential recommendation model combining mamba and multi-task graph learning
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DOI：10.1016/j.eswa.2026.131219.png)
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摘要
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• Adaptive masking reconstructs item-item graphs to enhance feature generalization. • Message passing updates user-item graph to capture multi-level relational features. • Mamba sequence modeling with multi-task learning improves recommendation results.
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
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Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations用于顺序推荐的深度学习: 算法、影响因素和评估
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