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English(EN) Tackling the Modality Inhibitance Effect with Wavelet-based Modality Integration in Multi-modal Recommendation

新的小波方法解决推荐系统中的多模态抑制问题

研究人员在多模态推荐系统中发现了一种称为“多模态抑制效应”的现象,即当从双模态配置过渡到三模态配置时,性能会显著下降。这种下降是由于新特征引入了过多的信息冗余造成的。为了解决这个问题,提出了一种新的基于多尺度小波的方法MMWave,它利用小波分解从每个模态中提取基本、非冗余的特征。该系统还包含一个多模态图学习模块来捕获项目特征之间的潜在关联,以及一个自适应模态调节器以实现有效融合。 AI

影响 这项研究可以提高利用多种数据类型的推荐系统的准确性和效率。

排序理由 关于多模态推荐系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的小波方法解决推荐系统中的多模态抑制问题

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关于多模态推荐系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Andy W. H. Khong ·

    利用小波变换模态集成解决多模态推荐中的模态抑制效应

    Harnessing rich modality features has recently gained prominence in multi-modal recommendation systems (MRSs). To delineate user modality preferences, existing works focus on modality fusion, which can be achieved via attentive summation or concatenation. In this work, we introdu…