用动机推理重构冷启动物品关系，提升多模态推荐效果
MOTIF: Motivation-guided Topology Inference for Cold-start Multimodal Recommendation
- 通过大模型分析用户动机，挖掘隐含语义
- 重建可迁移的物品拓扑结构，提升冷启动表现
- 适合做冷启动或多模态推荐系统的研究者
冷启动多模态推荐面临三大耦合挑战：(i) 交互稀疏导致用户意图模糊，(ii) 冷门物品拓扑孤立，(iii) 基于相似性的物品图易引发语义漂移。为此，我们提出 MOTIF 框架，融合语义动机推理、知识增强图重建、加权图对比学习与语义-结构对齐。该方法利用离线大模型推理生成动机语义，重构可迁移的物品间拓扑关系，并在不注入生成文本的前提下学习鲁棒图嵌入。在三个多模态基准数据集上的实验表明，MOTIF 在各类基线（包括图方法、多模态方法、冷启动方法及 LLM 增强方法）上均取得一致提升，相对最强近期基线最高达 6.07% 的相对增益。
原文摘要 · Abstract (English)
Cold-start multimodal recommendation faces three coupled challenges: (i) sparse interactions obscure user intent, (ii) cold items remain topologically isolated, and (iii) similarity-based item graphs may cause semantic drift. To address these issues, we propose MOTIF, a Motivation-guided Topology Inference framework for cold-start multimodal recommendation. MOTIF integrates Semantic Motivation Reasoning, Knowledge-enhanced Graph Reconstruction, Weighted Graph Contrastive Learning, and Semantic-Structural Alignment. It uses offline LLM reasoning to infer motivation semantics, reconstructs transferable item-item topology, and learns robust graph embeddings without injecting generated text into prediction. Experiments on three multimodal benchmarks show consistent gains over graph-based, multimodal, cold-start, and LLM-enhanced baselines, with up to 6.07% relative improvement over the strongest recent baseline.
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