Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significant attention. However, existing multimodal recommendations overlook the critical role of user representation initialization. Unlike items, which are naturally associated with rich modality information, users lack such inherent information. Consequently, item representations initialized based on meaningful modality information and user representations initialized randomly exhibit a significant semantic gap. To this end, we propose a Semantically Guaranteed User Representation Initialization (SG-URInit). SG-URInit constructs the initial representation for each user by integrating both the modality features of the items they have interacted with and the global features of their corresponding clusters. SG-URInit enables the initialization of semantically enriched user representations that effectively capture both local (item-level) and global (cluster-level) semantics. Our SG-URInit is training-free and model-agnostic, meaning it can be seamlessly integrated into existing multimodal recommendation models without incurring any additional computational overhead during training. Extensive experiments on multiple real-world datasets demonstrate that incorporating SG-URInit into advanced multimodal recommendation models significantly enhances recommendation performance. Furthermore, the results show that SG-URInit can further alleviate the item cold-start problem and also accelerate model convergence, making it an efficient and practical solution for multimodal recommendations.

翻译：摘要：近年来，利用多样化模态信息缓解数据稀疏性并提升推荐精度的多模态推荐技术取得了显著进展。然而，现有方法忽视了用户表示初始化的关键作用。不同于天然关联丰富模态信息的物品，用户缺乏此类固有信息。因此，基于有意义模态信息初始化的物品表征与随机初始化的用户表征之间存在显著语义鸿沟。针对这一问题，本文提出了一种语义保障的用户表示初始化方法（SG-URInit）。该方法通过整合用户交互物品的模态特征及其对应聚类的全局特征，为每个用户构建初始表征。SG-URInit能够初始化语义丰富的用户表示，同时有效捕获局部（物品级）与全局（聚类级）语义。所提出的SG-URInit无需训练且与模型无关，可无缝集成至现有推荐模型中且不产生额外训练计算开销。在多个真实数据集上的大量实验表明，将SG-URInit嵌入先进的多模态推荐模型能显著提升推荐性能。此外，实验结果证明SG-URInit可进一步缓解物品冷启动问题并加速模型收敛，使其成为多模态推荐领域高效且实用的解决方案。