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.

翻译：食物推荐系统作为数字生活服务领域的关键组成部分，旨在帮助用户发现符合其独特饮食偏好的食谱与食品。通常，多模态描述为每个食谱提供了详尽的特征画像，从而确保推荐兼具个性化与准确性。我们对两个数据集的初步研究表明，在刻画交互关系时，与ID特征相比，预训练的多模态稠密表征可能导致性能下降。这一观察意味着ID特征在建模交互式协同信号方面具有相对优势。因此，当前前沿方法将多模态信息作为补充特征来增强ID特征，却忽略了食谱间潜在的语义关联。为纠正这一问题，我们提出了CLUSSL——一个采用聚类与自监督学习的新型食物推荐框架。具体而言，CLUSSL为每个具有离散/连续特征的模态构建专属的模态特定图，从而将语义特征转化为结构表征。此外，CLUSSL通过图卷积操作获取与不同模态相关的食谱表征。我们提出了一个自监督学习目标，以促进从不同单模态图得到的食谱表征之间的独立性。在真实数据集上的全面实验证实，CLUSSL在性能上持续超越当前最先进的推荐基准模型。