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Research questionHow can multimodal recommenders handle conflicting visual or semantic signals when user–item interactions are sparse?Visual or semantic item signals can be misleading or inconsistent with users’ interaction patterns. Treating every modality as beneficial can distort recommendations, especially when collaborative evidence is limited.
AI
Information Retrieval
Machine Learning
Multimodal Models
Research Paper
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Latest papersRecent research connected to this question, newest first.Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal RecommendationThe source addresses multimodal recommendation affected by deceptive visual content and mismatched semantics, using collaborative user–item structure as the reference. Evidence comes from experiments on three real-world Amazon datasets, including tests under modality noise and item sparsity.research paper · Sep 2, 2026
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