  Multimodal recommender systems work by augmenting the representation of the
products in the catalogue through multimodal features extracted from images,
textual descriptions, or audio tracks characterising such products.
Nevertheless, in real-world applications, only a limited percentage of products
come with multimodal content to extract meaningful features from, making it
hard to provide accurate recommendations. To the best of our knowledge, very
few attention has been put into the problem of missing modalities in multimodal
recommendation so far. To this end, our paper comes as a preliminary attempt to
formalise and address such an issue. Inspired by the recent advances in graph
representation learning, we propose to re-sketch the missing modalities problem
as a problem of missing graph node features to apply the state-of-the-art
feature propagation algorithm eventually. Technically, we first project the
user-item graph into an item-item one based on co-interactions. Then,
leveraging the multimodal similarities among co-interacted items, we apply a
modified version of the feature propagation technique to impute the missing
multimodal features. Adopted as a pre-processing stage for two recent
multimodal recommender systems, our simple approach performs better than other
shallower solutions on three popular datasets.
