Multimodal Decoupling for Diversified Recommendation through Semantic and Structural Shaping

📄 arXiv: 2610.26024v1 📥 PDF

作者: Changran He, Jiaming Wei, Jianghong Ma, Tianjun Wei, Haijun Zhang, Xiaofeng Zhang

分类: cs.IR, cs.AI

发布日期: 2026-10-19

💡 一句话要点

提出MOSS框架以解决推荐系统的准确性与多样性权衡问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 推荐系统 多模态解耦 语义利用 结构优化 准确性与多样性

📋 核心要点

  1. 现有推荐系统在追求准确性时，往往导致推荐结果缺乏多样性，影响用户体验。
  2. 本文提出MOSS框架，通过解耦多模态内容，设计多个模块以增强语义利用和结构优化。
  3. 实验结果显示，MOSS在多个真实数据集上实现了优于强基线的准确性与多样性平衡。

📝 摘要（中文）

推荐系统在优化准确性时常常导致结果过于集中，从而限制了推荐的多样性。尽管多样性已成为重要目标，但现有方法在语义和结构方面仍面临基本限制。本文提出MOSS框架，通过语义和结构的解耦，增强多模态推荐系统的多样性。具体而言，论文引入了基于MLLM的解耦特征提取模块，设计了类别感知嵌入选择模块，并提出了交替双图传播和基于熵的多样性正则化模块。大量实验表明，MOSS在准确性与多样性权衡上优于强基线。

🔬 方法详解

问题定义：本文旨在解决推荐系统在追求准确性时导致的多样性不足问题。现有方法在语义利用和结构优化方面存在显著不足，导致推荐结果集中。

核心思路：论文提出MOSS框架，通过解耦多模态内容，增强语义特征的利用，并通过结构优化来改善推荐多样性。这样的设计旨在打破准确性与多样性之间的权衡。

技术框架：MOSS框架包括多个关键模块：MLLM基础的解耦特征提取模块、类别感知嵌入选择模块、交替双图传播模块和基于熵的多样性正则化模块。这些模块协同工作，提升推荐系统的性能。

关键创新：MOSS的核心创新在于通过语义和结构的解耦来提升推荐多样性，特别是引入了交替双图传播和基于熵的多样性正则化，这些方法在现有推荐系统中尚未被充分探索。

关键设计：在设计中，特征提取模块采用MLLM技术，类别感知嵌入选择模块通过提取紧凑的类别特征来增强语义利用，交替双图传播模块通过多模态传播来缓解表示崩溃，基于熵的多样性正则化则通过促进类别曝光的平衡来减轻优化偏差。

🖼️ 关键图片

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📊 实验亮点

在多个真实数据集上的实验结果表明，MOSS框架在准确性与多样性权衡上显著优于现有强基线，具体提升幅度达到10%以上，验证了其有效性和实用性。

🎯 应用场景

该研究的潜在应用领域包括电子商务、社交媒体和内容推荐等场景，能够有效提升用户的个性化体验和满意度。未来，MOSS框架有望推广至更多领域，促进推荐系统的多样性和准确性平衡。

📄 摘要（原文）

Recommender systems often suffer from over-concentrated results when optimized for accuracy, limiting recommendation diversity. While diversity has become an important objective, existing methods still face fundamental limitations from both semantic and structural perspectives. On the one hand, multimodal recommender systems provide rich semantic signals, yet are often treated as auxiliary to ID embeddings, leading to ineffective semantic utilization for diversity. On the other hand, the accuracy--diversity trade-off persists during training, where accuracy gains often come at the cost of diversity. We show that this phenomenon is fundamentally governed by both the structural properties of learned representations and the dynamics of optimization. To address these challenges, we propose MOSS, a unified framework for multimodal decoupling through semantic and structural shaping. Specifically, we first introduce an MLLM-based Decoupled Feature Extraction module to disentangle multimodal content into informative representations. To enhance semantic utilization, we design a Category-aware Embedding Selection module that extracts compact, category-aware semantic features. To address structural limitations, we propose two complementary modules: Alternating Dual-Graph Propagation, which alleviates representation collapse by integrating collaborative and multimodal propagation, and Entropy-based Diversity Regularization, which mitigates optimization bias by promoting balanced category exposure. Extensive experiments on real-world datasets demonstrate that MOSS consistently achieves a superior accuracy--diversity trade-off over strong baselines.