Denoising Diffusion Model-Driven Adaptive Estimation of Distribution Algorithm Integrating Multi-Modal Data

Lin Bao

doi:10.3390/math13233777

2025-11-25

DOI：10.3390/math13233777

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摘要

En 中文

个性化多模态数据的搜索与推荐算法已引起广泛关注。然而，现有方法在有效整合多源信息以及在复杂优化问题中进行全局搜索方面往往面临挑战。为解决这些局限性，本文提出了一种基于去噪扩散模型驱动的分布估计算法，并整合了多模态数据。广泛收集了多模态用户生成内容，例如用户的交互行为、类别标签、文本评论、图像、社交网络关系等。通过学习多模态数据的融合表示，建立了基于去噪扩散模型的用户兴趣偏好模型，该模型提取用户偏好特征。在分布估计算法的框架下，提出了基于用户偏好和自适应分布估计算法的代理模型策略。设计了一种代理模型驱动的自适应分布估计算法，以匹配用户的认知体验和行为模式，从而增强个性化搜索算法的优化能力。此外，建立了一个动态模型管理机制，用于利用新的可用模态信息更新用户兴趣偏好模型，该机制追踪实际场景中用户兴趣偏好的变化。它帮助用户从大规模信息源中高效地筛选出符合其偏好的项目。在公开数据集上的大量实验证明了所提出算法的可行性、有效性和优越性，确认了其在个性化推荐算法的搜索效率和推荐性能方面的改进。

Keyword:

multi-modal data

personalized search

denoising diffusion model

estimation of distribution algorithm

surrogate model

期刊

Mathematics

IF:

2.2

论文数:

2.9K

被引数:

3.6W

机构

shantou university

学者数:

2.7K

论文数: 890

被引数: 0

jiangsu university of science & technology

学者数:

9.0K

论文数: 6.9K

被引数: 9

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