AI 中文总结
MGDiff是结合DSG与PAG机制的多兴趣序列推荐模型，在四个数据集上较基线模型表现更优，可生成准确且无流行度偏差的多样化推荐。
AI 中文摘要
我们提出了一种新颖的多兴趣序列推荐框架——掩码GNN引导扩散模型（MGDiff），旨在在扩散过程中生成准确、无偏的用户兴趣信息。首先，我们提出了语义增强的双层语义引导（DSG）框架，该框架将引导分解为两个协同阶段：提取潜在物品语义和解耦多维用户意图。我们设计了一种权重自适应掩码图神经网络，用于重构缺失链接以揭示表面共现之外的深层物品关系，同时动态多专家网络将用户偏好投影到不同的语义子空间以抑制无关干扰。这种分层设计产生了结构化引导，显著提高了扩散模型的生成准确性。其次，我们提出了流行度感知引导（PAG）机制，该机制对扩散模型的输出进行空间几何调整：通过将物品流行度作为可微调整信号重新校准相似度指标，使扩散模型能够生成无流行度偏差的多样化推荐。最后，我们在四个广泛使用的数据集上将MGDiff与多个基线模型进行比较，证明了其卓越性能并验证了其有效性。
英文摘要
We propose a novel Multi-Interest Sequence Recommendation Framework with \underline{M}asking \underline{G}NN-Guided \underline{Diff}usion Model (MGDiff), designed to generate accurate, bias-free user interest information during the diffusion process. First, we propose a semantics-enhanced Dual-layer Semantic Guidance (DSG) framework, which decomposes guidance into two synergistic stages: extracting latent item semantics and decoupling multidimensional user intent. We design a Weight-adaptive Masking Graph Neural Network reconstructs missing links to uncover deep item relationships beyond superficial co-occurrence, while a Dynamic Multi-Expert Network projects user preferences into distinct semantic subspaces to suppress irrelevant interference. This hierarchical design yields structured guidance that significantly improves the generation accuracy of diffusion models. Second, We propose a Popularity-Aware Guidance (PAG) mechanism that performs spatial geometric adjustments on the outputs of diffusion models: by using item popularity as a differentiable adjustment signal to recalibrate similarity metrics, we enable DMs to generate diverse recommendations free from popularity bias. Finally, we compare MGDiff with multiple baseline models across four widely used datasets, demonstrating its superior performance and validating its effectiveness.
Comments8 pages, 4 figures
