AI 中文总结
针对冷启动多模态推荐的三大耦合挑战，提出MOTIF框架，整合四类技术实现拓扑推断，在三个多模态基准上较各类基线取得最高6.07%的相对提升。
AI 中文摘要
冷启动多模态推荐面临三个耦合挑战：一是稀疏交互模糊用户意图，二是冷启动项目在拓扑上孤立，三是基于相似度的项目图可能引发语义漂移。为解决这些问题，本文提出MOTIF——面向冷启动多模态推荐的动机引导拓扑推断框架。该框架整合语义动机推理、知识增强图重构、加权图对比学习及语义-结构对齐，利用离线大语言模型（LLM）推理推断动机语义，重构可迁移的项目-项目拓扑，学习鲁棒图嵌入且不将生成文本注入预测环节。在三个多模态基准上的实验显示，该方法较图类、多模态、冷启动及LLM增强基线均取得一致提升，较最新最强基线的相对提升最高达6.07%。
英文摘要
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.
Comments15 pages, 3 figures, 7 tables. Accepted at WISE 2026
