  Food recommendation systems serve as pivotal components in the realm of
digital lifestyle services, designed to assist users in discovering recipes and
food items that resonate with their unique dietary predilections. Typically,
multi-modal descriptions offer an exhaustive profile for each recipe, thereby
ensuring recommendations that are both personalized and accurate. Our
preliminary investigation of two datasets indicates that pre-trained
multi-modal dense representations might precipitate a deterioration in
performance compared to ID features when encapsulating interactive
relationships. This observation implies that ID features possess a relative
superiority in modeling interactive collaborative signals. Consequently,
contemporary cutting-edge methodologies augment ID features with multi-modal
information as supplementary features, overlooking the latent semantic
relations between recipes. To rectify this, we present CLUSSL, a novel food
recommendation framework that employs clustering and self-supervised learning.
Specifically, CLUSSL formulates a modality-specific graph tailored to each
modality with discrete/continuous features, thereby transforming semantic
features into structural representation. Furthermore, CLUSSL procures recipe
representations pertinent to different modalities via graph convolutional
operations. A self-supervised learning objective is proposed to foster
independence between recipe representations derived from different unimodal
graphs. Comprehensive experiments on real-world datasets substantiate that
CLUSSL consistently surpasses state-of-the-art recommendation benchmarks in
performance.
