  Multimodal recommendation exploits the rich multimodal information associated
with users or items to enhance the representation learning for better
performance. In these methods, end-to-end feature extractors (e.g.,
shallow/deep neural networks) are often adopted to tailor the generic
multimodal features that are extracted from raw data by pre-trained models for
recommendation. However, compact extractors, such as shallow neural networks,
may find it challenging to extract effective information from complex and
high-dimensional generic modality features. Conversely, DNN-based extractors
may encounter the data sparsity problem in recommendation. To address this
problem, we propose a novel model-agnostic approach called Semantic-guided
Feature Distillation (SGFD), which employs a teacher-student framework to
extract feature for multimodal recommendation. The teacher model first extracts
rich modality features from the generic modality feature by considering both
the semantic information of items and the complementary information of multiple
modalities. SGFD then utilizes response-based and feature-based distillation
loss to effectively transfer the knowledge encoded in the teacher model to the
student model. To evaluate the effectiveness of our SGFD, we integrate SGFD
into three backbone multimodal recommendation models. Extensive experiments on
three public real-world datasets demonstrate that SGFD-enhanced models can
achieve substantial improvement over their counterparts.
