Multimodal recommendation has attracted extensive attention by leveraging heterogeneous modality information to alleviate data sparsity and improve recommendation accuracy. Existing methods have attempted to replace ID embeddings with multimodal features and have achieved promising preliminary results. However, these methods still exhibit the following two limitations: (1) the reconstructed ID representations remain relatively static and fail to fully exploit multimodal semantics; and (2) the graph learning process is insufficient in mining latent long-tail semantic relations and is easily affected by popularity bias. To address these issues, we propose a novel method named Modality-Aware Identity Construction and Counterfactual Structure Learning for ID-free Multimodal Recommendation (MAIL). Specifically, we design a modality-aware identity construction module that dynamically modulates positional encodings with multimodal semantics to construct content-aware ID-free identity representations. Then, we propose a counterfactual structure learning paradigm that mines low-exposure semantic neighbors via popularity penalization and alleviates popularity bias. Extensive experiments are conducted on five public Amazon datasets. Experimental results show that MAIL achieves average improvements of 7.81% in Recall@10 and 12.81% in NDCG@10 compared with the baseline models. Our code is available at https://github.com/HubuKG/MAIL.

翻译：多模态推荐通过利用异构模态信息缓解数据稀疏性并提升推荐准确度而受到广泛关注。现有方法尝试用多模态特征替换ID嵌入，并取得了初步的优异结果。然而，这些方法仍存在以下两个局限性：（1）重建的ID表示相对静态，未能充分利用多模态语义；（2）图学习过程在挖掘潜在的长尾语义关系方面存在不足，且易受流行度偏差影响。为解决这些问题，我们提出了一种名为“模态感知身份构建与反事实结构学习的无ID多模态推荐”（MAIL）的新方法。具体而言，我们设计了一个模态感知身份构建模块，通过多模态语义动态调制位置编码，以构建内容感知的无ID身份表示。随后，我们提出了一种反事实结构学习范式，通过流行度惩罚机制挖掘低曝光语义邻居，并缓解流行度偏差。我们在五个公开的Amazon数据集上进行了大量实验。实验结果表明，与基线模型相比，MAIL在Recall@10和NDCG@10上分别平均提升了7.81%和12.81%。我们的代码已开源至https://github.com/HubuKG/MAIL。