D3ER：基于解耦与蒸馏的动态集成多模态推荐方法
D3ER: Supporting Multi-Modal Recommendation via Disentangle and Distillation-based Dynamic Ensemble
AI总结：
该研究针对多模态推荐中同质性与异质性判别信息联合学习的缺陷，提出D3ER方法，引入梯度提升并结合知识蒸馏与全局校正正则化，在真实数据集上验证了其优越性。
AI中文摘要：
将多模态间共享的物品信息整合为融合表示的多模态推荐（MR），已被证实比传统单模态推荐效果更好。尽管已有多项尝试提取各模态独有的判别信息，但现有方法存在核心局限：模态同质性判别信息（HOI）与模态异质性判别信息（HEI）的联合学习往往会削弱两者各自的有效性。为弥补这一缺陷，我们提出一种名为D3ER（基于解耦与蒸馏的动态集成多模态推荐）的新方法。我们首次将梯度提升引入MR，以形式化交替学习HOI与HEI的优化目标，该设计使每种信息对应的模型能专注于自身擅长的样本，从而推动专业化优化。此外，为缓解梯度提升固有的高存储成本与局部最优风险，我们通过知识蒸馏和全局校正正则化增强了框架。在流行的真实世界数据集上开展的实验证实了所提方法在MR任务上的优越性。
英文摘要：
Incorporating items' information shared among multiple modalities into a fused representation, multi-modal recommendation (MR) has demonstrated documented success than canonical unimodal recommendation. Although several attempts have been made to extract the discriminative information unique in each modality, existing methods suffer from a core limitation: the joint learning of modal-homogeneity discriminative information (HOI) and modal-heterogeneity discriminative information (HEI) tends to weaken their individual effectiveness. To remedy this deficiency, we propose a novel method, dubbed Disentangle and Distillation-based Dynamic Ensemble for multi-modal Recommendation (D3ER). We introduce gradient boosting into MR for the first time to formalize the optimization objective for alternately learning HOI and HEI. This design enables models dedicated to each type of information to focus on their proficient samples, thereby promoting specialized optimization. Furthermore, to mitigate the inherent high storage cost and risk of local optima in gradient boosting, we enhance our framework with knowledge distillation and a global correction regularization. Experiments on prevalent real-world datasets confirm the superiority of our proposed method on MR.
