  In most E-commerce platforms, whether the displayed items trigger the user's
interest largely depends on their most eye-catching multimodal content.
Consequently, increasing efforts focus on modeling multimodal user preference,
and the pressing paradigm is to incorporate complete multimodal deep features
of the items into the recommendation module. However, the existing studies
ignore the mismatch problem between multimodal feature extraction (MFE) and
user interest modeling (UIM). That is, MFE and UIM have different emphases.
Specifically, MFE is migrated from and adapted to upstream tasks such as image
classification. In addition, it is mainly a content-oriented and
non-personalized process, while UIM, with its greater focus on understanding
user interaction, is essentially a user-oriented and personalized process.
Therefore, the direct incorporation of MFE into UIM for purely user-oriented
tasks, tends to introduce a large number of preference-independent multimodal
noise and contaminate the embedding representations in UIM.
  This paper aims at solving the mismatch problem between MFE and UIM, so as to
generate high-quality embedding representations and better model multimodal
user preferences. Towards this end, we develop a novel model, MEGCF. The UIM of
the proposed model captures the semantic correlation between interactions and
the features obtained from MFE, thus making a better match between MFE and UIM.
More precisely, semantic-rich entities are first extracted from the multimodal
data, since they are more relevant to user preferences than other multimodal
information. These entities are then integrated into the user-item interaction
graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is
constructed to perform message propagation over the graph, in order to capture
both high-order semantic correlation and collaborative filtering signals.
