用知识图谱融合多模态信息，提升推荐精准度
Knowledge graph-based personalized multimodal recommendation fusion framework
- 通过跨图跨模态注意力实现图文特征细粒度融合
- 利用图注意力网络传播高阶邻接关系，捕捉深层用户兴趣
- 适合研究多模态推荐与知识图谱融合的从业者
在信息过载的时代，人工智能的快速发展使推荐系统变得不可或缺。基于协同过滤或单一属性的传统推荐方法难以捕捉用户复杂的兴趣偏好。知识图谱与多模态数据的结合能更丰富、精确地表征用户与物品。本文综述现有多模态知识图谱推荐框架，指出其在模态交互与高阶依赖建模方面的不足。提出跨图跨模态互信息驱动的统一知识图谱学习与推荐框架（CrossGMMI-DUKGLR），采用预训练视觉-文本对齐模型提取特征，通过多头交叉注意力实现细粒度模态融合，并利用图注意力网络传播高阶邻接信息。
原文摘要 · Abstract (English)
In the contemporary age characterized by information abundance, rapid advancements in artificial intelligence have rendered recommendation systems indispensable. Conventional recommendation methodologies based on collaborative filtering or individual attributes encounter deficiencies in capturing nuanced user interests. Knowledge graphs and multimodal data integration offer enhanced representations of users and items with greater richness and precision. This paper reviews existing multimodal knowledge graph recommendation frameworks, identifying shortcomings in modal interaction and higher-order dependency modeling. We propose the Cross-Graph Cross-Modal Mutual Information-Driven Unified Knowledge Graph Learning and Recommendation Framework (CrossGMMI-DUKGLR), which employs pre-trained visual-text alignment models for feature extraction, achieves fine-grained modality fusion through multi-head cross-attention, and propagates higher-order adjacency information via graph attention networks.
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