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This paper introduces MOTIF, a novel framework that tackles the challenges of cold-start multimodal recommendation by addressing sparse interactions, topological isolation of cold items, and semantic drift in similarity-based item graphs. By leveraging Semantic Motivation Reasoning and Knowledge-enhanced Graph Reconstruction, MOTIF effectively infers user motivations and reconstructs item relationships, leading to more accurate recommendations. Experimental results demonstrate that MOTIF outperforms existing methods, achieving up to a 6.07% relative improvement over the best baseline on three multimodal benchmarks.
Cold-start multimodal recommendation can be dramatically improved by inferring user motivations and reconstructing item relationships, leading to significant performance gains.
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.