MOTIF: Motivation-guided Topology Inference for Cold-start Multimodal Recommendation
MOTIF, a Motivation-guided Topology Inference framework for cold-start multimodal recommendation, uses offline LLM reasoning to infer motivation semantics, reconstructs transferable item-item topology, and learns robust graph embeddings without injecting generated text into prediction.