A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions have been proposed, most fail to account for discrepancies between knowledge extracted through isolated feature extraction and its application in recommendation tasks. Specifically, multimodal feature extraction does not incorporate task-specific prior knowledge, while downstream recommendation tasks typically use these features as auxiliary information. This misalignment often introduces biases in model fitting and degrades performance, a phenomenon we refer to as the curse of knowledge. To address this challenge, we propose a knowledge soft integration framework designed to balance the utilization of multimodal features with the biases they may introduce. The framework, named Knowledge Soft Integration (KSI), comprises two key components: the Structure Efficient Injection (SEI) module and the Semantic Soft Integration (SSI) module. The SEI module employs a Refined Graph Neural Network (RGNN) to model inter-modal correlations among items while introducing a regularization term to minimize redundancy in user and item representations. In parallel, the SSI module utilizes a self-supervised retrieval task to implicitly integrate multimodal semantic knowledge, thereby enhancing the semantic distinctiveness of item representations. We conduct comprehensive experiments on three benchmark datasets, demonstrating KSI's effectiveness. Furthermore, these results underscore the ability of the SEI and SSI modules to reduce representation redundancy and mitigate the curse of knowledge in multimodal recommendation systems.

翻译：当代推荐系统面临的核心挑战在于如何有效利用多模态内容以提升推荐个性化水平。尽管已有多种解决方案被提出，但大多数未能考虑通过孤立特征提取获取的知识与其在推荐任务中应用之间的差异。具体而言，多模态特征提取过程未融入任务特定的先验知识，而下游推荐任务通常仅将这些特征作为辅助信息使用。这种错配常导致模型拟合偏差并降低性能，我们将此现象称为知识诅咒。为应对这一挑战，本文提出一种知识软集成框架，旨在平衡多模态特征的利用与其可能引入的偏差。该框架命名为知识软集成（KSI），包含两个核心组件：结构高效注入（SEI）模块与语义软集成（SSI）模块。SEI模块采用精炼图神经网络（RGNN）建模物品间的跨模态关联，同时引入正则化项以最小化用户和物品表征的冗余度。与此同时，SSI模块通过自监督检索任务隐式整合多模态语义知识，从而增强物品表征的语义区分性。我们在三个基准数据集上进行了全面实验，验证了KSI框架的有效性。此外，实验结果进一步证实了SEI与SSI模块能够有效降低表征冗余度，缓解多模态推荐系统中的知识诅咒问题。