One of the main challenges in modern recommendation systems is how to effectively utilize multimodal content to achieve more personalized recommendations. Despite various proposed solutions, most of them overlook the mismatch between the knowledge gained from independent feature extraction processes and downstream recommendation tasks. Specifically, multimodal feature extraction processes do not incorporate prior knowledge relevant to recommendation tasks, while recommendation tasks often directly use these multimodal features as side information. This mismatch can lead to model fitting biases and performance degradation, which this paper refers to as the \textit{curse of knowledge} problem. To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about. To achieve this, we put forward a Knowledge Soft Integration framework for the multimodal recommendation, abbreviated as KSI, which is composed of the Structure Efficiently Injection (SEI) module and the Semantic Soft Integration (SSI) module. In the SEI module, we model the modality correlation between items using Refined Graph Neural Network (RGNN), and introduce a regularization term to reduce the redundancy of user/item representations. In the SSI module, we design a self-supervised retrieval task to further indirectly integrate the semantic knowledge of multimodal features, and enhance the semantic discrimination of item representations. Extensive experiments on three benchmark datasets demonstrate the superiority of KSI and validate the effectiveness of its two modules.

翻译：现代推荐系统面临的主要挑战之一是如何有效利用多模态内容实现更个性化的推荐。尽管已有多种解决方案被提出，但大多数方法忽视了从独立特征提取过程中获得的知识与下游推荐任务之间的不匹配问题。具体而言，多模态特征提取过程未融入与推荐任务相关的先验知识，而推荐任务却常将这些多模态特征直接作为辅助信息使用。这种不匹配会导致模型拟合偏差及性能下降，本文将此现象称为"知识诅咒"问题。为解决该问题，我们提出通过知识软融合来平衡多模态特征利用与其带来的知识诅咒效应。为此，我们构建了面向多模态推荐的知识软融合框架KSI，其包含结构高效注入模块与语义软融合模块。在结构高效注入模块中，我们采用精细化图神经网络建模物品间的模态关联，并引入正则化项降低用户/物品表征的冗余性；在语义软融合模块中，我们设计自监督检索任务以间接融合多模态特征的语义知识，同时增强物品表征的语义区分性。在三个基准数据集上的大量实验证明了KSI的优越性，并验证了两个模块的有效性。