{"type":"ScholarlyArticle","title":"Three Minds, One Student: Online Multi-Teacher Knowledge Distillation for Multimodal Recommenders","authors":[{"name":"Hangtong Xu","slug":"hangtong-xu","given":"Hangtong","family":"Xu","affiliation":""},{"name":"Yuanbo Xu","slug":"yuanbo-xu","given":"Yuanbo","family":"Xu","affiliation":""},{"name":"En Wang","slug":"en-wang","given":"En","family":"Wang","affiliation":""}],"abstract":"Existing multimodal recommendation models using complex fusion mechanisms (e.g., attention) or multi-stage processes (e.g., early or late fusion) integrate different modalities. However, attention-based adaptive fusion is prone to shortcut learning, where dominant collaborative signals (ID) can overshadow other modalities. This dominance affects the entire fusion process: early fusion often amplifies biases driven by identity, while late fusion struggles to extract preference-relevant signals from misaligned modalities, even with alignment regularization. To address these issues, we propose Multi-Teacher Single-Student Online Distillation for Multimodal Recommendation (MTS2-4MM), which reframes the multimodal recommendation task from direct fusion to controllable knowledge transfer. Specifically, we construct multiple teachers to specialize in complementary perspectives, and a unified student distills their guidance via objectives at both the ranking and representation levels. This design explicitly controls modality contributions, improves robustness to modality noise and misalignment. Furthermore, we design a Modality-specific Preference Extractor to explicitly extract user preferences across different modalities equally. Extensive experiments across five real-world datasets demonstrate that MTS2-4MM consistently outperforms state-of-the-art baselines, achieving improvements of up to 7.22%.","doi":"10.24963/ijcai.2026/348","url":"https://ijcai.org/proceedings/2026/348","pdf":"https://ijcai.org/proceedings/2026/0348.pdf","markdown":"https://ijcai.org/proceedings/2026/348.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/348","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":348,"pages":{"first":3133,"last":3141},"keywords":["Data Mining: Information retrieval","Data Mining: Collaborative filtering","Data Mining: Recommender systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}