  Multimedia content is of predominance in the modern Web era. Investigating
how users interact with multimodal items is a continuing concern within the
rapid development of recommender systems. The majority of previous work focuses
on modeling user-item interactions with multimodal features included as side
information. However, this scheme is not well-designed for multimedia
recommendation. Specifically, only collaborative item-item relationships are
implicitly modeled through high-order item-user-item relations. Considering
that items are associated with rich contents in multiple modalities, we argue
that the latent item-item structures underlying these multimodal contents could
be beneficial for learning better item representations and further boosting
recommendation. To this end, we propose a LATent sTructure mining method for
multImodal reCommEndation, which we term LATTICE for brevity. To be specific,
in the proposed LATTICE model, we devise a novel modality-aware structure
learning layer, which learns item-item structures for each modality and
aggregates multiple modalities to obtain latent item graphs. Based on the
learned latent graphs, we perform graph convolutions to explicitly inject
high-order item affinities into item representations. These enriched item
representations can then be plugged into existing collaborative filtering
methods to make more accurate recommendations. Extensive experiments on three
real-world datasets demonstrate the superiority of our method over
state-of-the-art multimedia recommendation methods and validate the efficacy of
mining latent item-item relationships from multimodal features.
