面向多模态推荐的信息引导型选择性模态-兴趣对齐方法
Information-Guided Selective Modality-Interest Alignment for Multimodal Recommendation
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中文总结 AI 辅助
针对多模态推荐中模态信号选择不明确的问题，提出AMUR框架，通过优化模态图结构并选择性对齐跨模态兴趣语义，提升推荐性能且保留互补信息，经实验验证有效。
中文摘要 AI 辅助
多模态推荐（MMRec）旨在利用来自多种模态的丰富物品内容提升推荐性能。然而，直接融入所有模态信息未必能更好地建模用户偏好，因为用户兴趣通常仅与部分模态信号相关，其余信号可能与用户偏好对齐度较低，甚至引入噪声。尽管近期的MMRec方法通过不变学习、注意力机制、图优化或对比学习改进了模态利用，但它们的对齐过程往往是隐式的或启发式的，缺乏选择与用户兴趣更匹配的模态信号的明确目标。本文提出AMUR，一种面向多模态推荐的信息引导型选择性模态-兴趣对齐框架。受信息论视角启发，AMUR旨在增强与用户兴趣更相关的模态信息，同时降低对齐度较低信号的影响。具体而言，AMUR首先针对用户行为优化模态图结构，然后选择性对齐跨模态间共享的与兴趣相关的语义。这使AMUR在提升模态-兴趣对齐度的同时，保留有用的模态特定互补信息。在三个真实世界数据集上开展的大量实验表明，AMUR相较于竞争基线具有有效性，代码可在指定URL获取。
英文摘要
Multimodal recommendation (MMRec) aims to enhance recommendation performance by leveraging rich item content from multiple modalities. However, directly incorporating all modality information does not necessarily lead to better preference modeling, since user interests are often more related to a subset of modality signals, while other signals may be weakly aligned with user preferences or even introduce noise. Although recent MMRec methods improve modality utilization through invariant learning, attention mechanisms, graph refinement, or contrastive learning, their alignment processes are often implicit or heuristic and lack a clear objective for selecting modality signals that better match user interests. In this paper, we propose AMUR, an information-guided selective modality-interest alignment framework for multimodal recommendation. Inspired by an information-theoretic view, AMUR aims to enhance modality information that is more related to user interests while reducing the influence of less aligned signals. Specifically, AMUR first refines modality graph structures towards user behavior, and then selectively aligns shared interest-related semantics across modalities. This enables AMUR to improve modality-interest alignment while preserving useful modality-specific complementary information. Extensive experiments on three real-world datasets demonstrate the effectiveness of AMUR over competitive baselines. The code is available at https://github.com/Wenze1/AMUR.
