AI 中文总结
研究多模态推荐中物品-物品图构建及应用，提出IIMRec框架构建高质量图，通过融合信号和NCER优化，并在推荐三阶段重用，以RIG、内容引导UI图扩展、INA三种方式使用，实验证明其性能优于基线，冷启动等条件下效果更佳。
AI 中文摘要
多模态推荐利用物品多模态特征和协作信号来捕捉用户偏好。虽然物品-物品图已成为先进模型的关键组成部分，但现有方法通常用有噪声的相似性边构建它们，并将其作用限于物品-物品表示传播的单一功能，潜力未充分挖掘。本文提出IIMRec框架，在预处理阶段构建单个高质量物品-物品图，并在推荐管道的三个阶段系统重用：表示增强、交互图增强和优化增强。该图通过融合语义和共现信号构建，然后经邻域一致性边重加权（NCER）优化，利用三元闭包原则放大结构可靠边并抑制虚假边。构建后，图以三种互补方式使用：（1）用残差II门（RIG）进行物品-物品传播以增强表示；（2）通过高置信度语义邻居引入虚拟用户-物品边来增强交互图；（3）II-邻居BPR增强（INA）将正物品的顶级邻居视为折扣软正例以增强优化。理论分析表明NCER降低谱噪声信号比，RIG收敛到非退化门控机制，INA产生更紧泛化界。在四个数据集上的大量实验表明IIMRec始终优于现有基线，运行更快且消耗更少GPU内存，在冷启动和稀疏交互条件下收益尤其显著。
英文摘要
Multimodal recommendation leverages item multimodal features alongside collaborative signals to capture user preferences. While item-item graphs have become a key building block in advanced models, existing methods typically construct them with noisy similarity edges and limit their role to a single function of item-item representation propagation, leaving substantial potential untapped. In this paper, we propose IIMRec, a framework that constructs a single high-quality item-item graph during preprocessing and systematically reuses it across three stages of the recommendation pipeline: representation enhancement, interaction graph enhancement, and optimization enhancement. The graph is built by fusing semantic and co-occurrence signals, then refined via Neighborhood Consistency Edge Reweighting (NCER), which applies the triadic closure principle to amplify structurally reliable edges and suppress spurious ones. Once constructed, the graph is leveraged in three complementary ways: (1) Item-item propagation with a Residual II Gate (RIG) that adaptively controls per-item absorption of semantic neighborhood signals for representation enhancement; (2) A content-guided UI graph expansion that introduces virtual user-item edges through high-confidence semantic neighbors for interaction graph enhancement; (3) II-Neighbor BPR Augmentation (INA) that treats top neighbors of positive items as discounted soft positives for optimization enhancement. We provide theoretical analysis showing that NCER reduces the spectral noise-to-signal ratio, RIG converges to a non-degenerate gating regime, and INA yields a tighter generalization bound. Extensive experiments on four datasets demonstrate that IIMRec consistently outperforms state-of-the-art baselines while running faster and consuming less GPU memory, with particularly strong gains under cold-start and sparse-interaction conditions.
CommentsAccepted by ACM MM 2026
