Recipe recommendation has become an essential task in web-based food platforms. A central challenge is effectively leveraging rich multimodal features beyond user-recipe interactions. Our analysis shows that even simple uses of multimodal signals yield competitive performance, suggesting that systematic enhancement of these signals is highly promising. We propose TESMR, a 3-stage framework for recipe recommendation that progressively refines raw multimodal features into effective embeddings through: (1) content-based enhancement using foundation models with multimodal comprehension, (2) relation-based enhancement via message propagation over user-recipe interactions, and (3) learning-based enhancement through contrastive learning with learnable embeddings. Experiments on two real-world datasets show that TESMR outperforms existing methods, achieving 7-15% higher Recall@10.

翻译：食谱推荐已成为网络食品平台中的关键任务。核心挑战在于如何有效利用用户-食谱交互之外丰富的多模态特征。我们的分析表明，即便是对多模态信号的简单运用也能取得竞争性表现，这表明对这些信号进行系统性增强极具潜力。我们提出TESMR——一个三阶段食谱推荐框架，通过以下步骤逐步将原始多模态特征精炼为有效嵌入：(1) 基于基础模型的多模态内容理解增强，(2) 通过用户-食谱交互的消息传播实现关系增强，(3) 基于可学习嵌入的对比学习实现学习增强。在两个真实世界数据集上的实验表明，TESMR优于现有方法，其Recall@10指标提升7-15%。