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arXiv · 2511.19176

From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation

Jeeho Shin·Kyungho Kim·Kijung Shin
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Abstract

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.

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Jeeho Shin, Kyungho Kim, Kijung Shin. 2026-04-22. From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation. https://arxiv.org/abs/2511.19176

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