  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.
