Scholarlink

<p>Social recommendation utilizes social connections as auxiliary information to deeply mine user preferences, thereby improving the recommendation performance. Existing methods often employ graph neural networks to encode social graphs. However, average aggregation may lead to node distortion, and diffusion models may reconstruct embeddings of multi-faceted interests and attributes that are misaligned with task-relevant directions. To address these limitations, this paper introduces a <Emphasis Type="Underline">C</Emphasis>onditional <Emphasis Type="Underline">G</Emphasis>uided <Emphasis Type="Underline">D</Emphasis>iffusion architecture in <Emphasis Type="Underline">L</Emphasis>atent <Emphasis Type="Underline">S</Emphasis>pace for social recommendation (CGDLS). Specifically, CGDLS first leverages singular value decomposition (SVD) to encode the social connection graph and the co-interacted items graph among users into the low-dimensional latent space. To alleviate noise distortion, CGDLS captures its key features by leveraging SVD to encode the social connection graph. During the reverse process, CGDLS incorporates the embedding of co-interacted items among users as conditional guidance. It guides the reverse process to reconstruct a highly task-relevant social connection embedding. Extensive experiments conducted on three social datasets demonstrate that CGDLS and its components outperform various state-of-the-art methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Conditional guided diffusion model in latent space for social recommendation

  • 期刊论文

  • 发表日期：

    2026年1月22日

查看全文
  • Yijun Hu,
  • Rui Tang,
  • Xian Mo

摘要

Social recommendation utilizes social connections as auxiliary information to deeply mine user preferences, thereby improving the recommendation performance. Existing methods often employ graph neural networks to encode social graphs. However, average aggregation may lead to node distortion, and diffusion models may reconstruct embeddings of multi-faceted interests and attributes that are misaligned with task-relevant directions. To address these limitations, this paper introduces a Conditional Guided Diffusion architecture in Latent Space for social recommendation (CGDLS). Specifically, CGDLS first leverages singular value decomposition (SVD) to encode the social connection graph and the co-interacted items graph among users into the low-dimensional latent space. To alleviate noise distortion, CGDLS captures its key features by leveraging SVD to encode the social connection graph. During the reverse process, CGDLS incorporates the embedding of co-interacted items among users as conditional guidance. It guides the reverse process to reconstruct a highly task-relevant social connection embedding. Extensive experiments conducted on three social datasets demonstrate that CGDLS and its components outperform various state-of-the-art methods.