Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for each user and item but hinder generalization to untrained data, and (iii) they assist in aligning and fusing multimodal features but may lead to representation shift. Based on these insights, this paper proposes IDFREE, an ID-free multimodal collaborative Filtering REcommEndation baseline. IDFREE replaces ID features with multimodal features and positional encodings to generate semantically meaningful ID-free embeddings. For ID-free multimodal collaborative filtering, it further proposes an adaptive similarity graph module to construct dynamic user-user and item-item graphs based on multimodal features. Then, an augmented user-item graph encoder is proposed to construct more effective user and item encoding. Finally, IDFREE achieves inter-multimodal alignment based on the contrastive learning and uses Softmax loss as recommendation loss. Basic experiments on three public datasets demonstrate that IDFREE outperforms existing ID-based MCFRec methods, achieving an average performance gain of 72.24% across standard metrics (Recall@5, 10, 20, 50 and NDCG@5, 10, 20, 50). Exploratory and extended experiments further validate our findings on the limitations of ID features in MCFRec. The code is released at https://github.com/G-H-Li/IDFREE.

翻译：现有的大多数多模态协同过滤推荐方法严重依赖ID特征和多模态内容来提升推荐性能。然而，本文揭示了ID特征虽然有效，但在多模态协同过滤推荐中的收益有限。因此，本文系统解构了ID特征的优缺点：(i) 它们提供初始嵌入但缺乏语义丰富性，(ii) 为每个用户和物品提供唯一标识符但阻碍对未训练数据的泛化能力，(iii) 协助对齐和融合多模态特征但可能导致表示偏移。基于这些洞察，本文提出了IDFREE——一种无ID的多模态协同过滤推荐基线模型。IDFREE用多模态特征和位置编码替代ID特征，以生成具有语义意义的无ID嵌入。对于无ID多模态协同过滤，进一步提出了自适应相似度图模块，基于多模态特征构建动态的用户-用户图和物品-物品图。随后，提出增强的用户-物品图编码器以构建更有效的用户和物品编码。最后，IDFREE基于对比学习实现多模态间对齐，并使用Softmax损失作为推荐损失。在三个公开数据集上的基础实验表明，IDFREE超越了现有的基于ID的多模态协同过滤推荐方法，在标准指标上平均性能提升达72.24%。探索性和扩展性实验进一步验证了我们对ID特征在多模态协同过滤推荐中局限性的发现。代码已发布于https://github.com/G-H-Li/IDFREE。