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arxiv: 2511.19176 · v3 · submitted 2025-11-24 · 💻 cs.LG · cs.IR

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

Jeeho Shin , Kyungho Kim , Kijung Shin This is my paper

Pith reviewed 2026-05-17 06:13 UTC · model grok-4.3

classification 💻 cs.LG cs.IR
keywords multimodal featuresrecipe recommendationembeddingsfoundation modelscontrastive learningmessage propagationuser interactions
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The pith

A three-stage framework refines raw multimodal recipe features into effective embeddings for improved recommendations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tries to establish that raw multimodal features in recipe data can be systematically turned into stronger embeddings by applying three progressive enhancements. A reader would care because recipe platforms already benefit from simple multimodal signals, so refining them further could lead to noticeably better suggestions for users. The stages build on each other: foundation models first extract content understanding from images and text, then message passing incorporates user-recipe relations, and contrastive learning finally sharpens the embeddings. If this holds, it offers a practical way to leverage available multimodal information more fully in recommendation systems.

Core claim

The central claim is that TESMR, by progressively refining raw multimodal features through content-based enhancement with foundation models, relation-based enhancement via message propagation over user-recipe interactions, and learning-based enhancement through contrastive learning, produces effective embeddings that deliver 7-15% higher Recall@10 than existing methods on two real-world datasets.

What carries the argument

The three-stage progressive enhancement process that converts raw features into refined embeddings by layering content comprehension, relational propagation, and contrastive refinement.

If this is right

  • Multimodal features gain effectiveness when enhanced in a staged manner rather than applied in isolation.
  • Recommendation accuracy improves when content understanding from foundation models is combined with interaction-based relations and learned adjustments.
  • The framework demonstrates consistent gains across multiple real datasets, suggesting robustness for practical deployment in food platforms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar staged enhancement could be tested in other multimodal recommendation settings, such as fashion or travel suggestions.
  • Removing the foundation model stage in experiments would clarify how much the initial content comprehension contributes to the overall gains.
  • The success implies that foundation models provide a reliable base for multimodal tasks even without domain-specific fine-tuning.

Load-bearing premise

That the three stages combine additively without causing information loss or overfitting while foundation models extract reliable multimodal understanding from recipe content.

What would settle it

An experiment that applies only the relation-based and learning-based stages without the content-based foundation model enhancement, and checks whether Recall@10 still shows the full 7-15% improvement over baselines.

Figures

Figures reproduced from arXiv: 2511.19176 by Jeeho Shin, Kijung Shin, Kyungho Kim.

Figure 2
Figure 2. Figure 2: Observation: A simple use of multimodal features [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of TESMR with (a) content-, (b) relation-, and (c) learning-based enhancement of multimodal features. After training, for each user–recipe pair (𝑢, 𝑟), the final score for recommendation is computed using inner products from both embedding types as follows: (e 𝑆 𝑟 ) ⊤e 𝑆 𝑢 + (e 𝐿 𝑟 ) ⊤e 𝐿 𝑢 . Comparison with existing multimodal recommenders. Ours leverages user reviews to generate user embeddings,… view at source ↗
Figure 4
Figure 4. Figure 4: Effects of 𝜏 and 𝜆𝐶𝐿 on the NDCG@20 of TESMR. References [1] 2025. Code, Datasets, and Appendix for "From Raw Features to Effective Em￾beddings: A Three-Stage Approach for Multimodal Recipe Recommendation". https://github.com/JHshin6688/TESMR [2] Yuzhuo Dang, Xin Zhang, Zhiqiang Pan, Yuxiao Duan, Wanyu Chen, Fei Cai, and Honghui Chen. 2025. MLLMRec: Exploring the Potential of Multimodal Large Language Mode… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes TESMR, a three-stage framework for multimodal recipe recommendation. Stage 1 applies content-based enhancement via foundation models with multimodal comprehension to raw features; stage 2 performs relation-based enhancement through message propagation over user-recipe interaction graphs; stage 3 refines embeddings with contrastive learning. The central empirical claim is that this progressive pipeline outperforms prior methods by 7-15% in Recall@10 on two real-world datasets, building on the observation that even naive multimodal feature use is already competitive.

Significance. If the performance gains are robustly verified, the work would offer a practical template for systematically upgrading multimodal signals in recommendation systems, particularly in content-rich domains such as recipes. The emphasis on progressive, non-destructive enhancement rather than end-to-end fusion could influence follow-on research on staged representation learning.

major comments (2)
  1. [Experiments section (and abstract)] The 7-15% Recall@10 improvement is presented as evidence that the three stages combine productively, yet the manuscript provides no stage-wise ablation results (e.g., foundation-model features alone versus full TESMR) or diagnostics for destructive interference such as noise amplification during graph propagation or embedding collapse under the contrastive objective. This omission leaves the incremental value of the full pipeline untested and is load-bearing for the central claim.
  2. [Experiments section] The abstract states that 'even simple uses of multimodal signals yield competitive performance,' which makes the added value of the relation-based and contrastive stages the key empirical question. Without reported statistical significance tests, variance across runs, or comparison against a strong multimodal baseline that already incorporates foundation-model features, the magnitude of the reported gains cannot be confidently attributed to the proposed three-stage design.
minor comments (2)
  1. [Method section] Notation for the three stages and the message-passing update rule should be introduced with explicit equations or pseudocode early in the method section to improve readability.
  2. [Experiments section] Dataset statistics (number of users, recipes, interactions, and sparsity) and the precise train/validation/test splits should be tabulated for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript arXiv:2511.19176. We address the major comments below and have incorporated revisions to enhance the experimental section as suggested.

read point-by-point responses

  1. Referee: [Experiments section (and abstract)] The 7-15% Recall@10 improvement is presented as evidence that the three stages combine productively, yet the manuscript provides no stage-wise ablation results (e.g., foundation-model features alone versus full TESMR) or diagnostics for destructive interference such as noise amplification during graph propagation or embedding collapse under the contrastive objective. This omission leaves the incremental value of the full pipeline untested and is load-bearing for the central claim.

    Authors: We concur with the referee that stage-wise ablations are necessary to substantiate the productive combination of the three stages. Accordingly, we have included these ablation results in the revised manuscript, comparing the performance of foundation-model enhanced features alone, the addition of relation-based propagation, and the complete TESMR pipeline. We have also provided diagnostics, including visualizations of embedding distributions and monitoring of contrastive loss to rule out collapse or noise issues. These new results support the incremental value of the full pipeline. revision: yes

  2. Referee: [Experiments section] The abstract states that 'even simple uses of multimodal signals yield competitive performance,' which makes the added value of the relation-based and contrastive stages the key empirical question. Without reported statistical significance tests, variance across runs, or comparison against a strong multimodal baseline that already incorporates foundation-model features, the magnitude of the reported gains cannot be confidently attributed to the proposed three-stage design.

    Authors: We agree that statistical tests and comparisons to strong baselines are important for attributing the gains to our design. In the revision, we report results with standard deviations over five independent runs and include paired t-test p-values to establish significance. Additionally, we have introduced a new baseline using only the content-based enhancement from foundation models within a standard multimodal recommender, against which TESMR demonstrates further improvements. This addresses the key empirical question regarding the added value of the subsequent stages. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical three-stage framework

full rationale

The paper presents TESMR as a progressive three-stage pipeline (content-based enhancement via foundation models, relation-based message passing over interactions, and contrastive learning refinement) whose value is established solely through empirical experiments on two real-world datasets with held-out evaluation. No mathematical derivations, equations, or predictions are shown that reduce by construction to fitted parameters or self-citations. Claims of 7-15% Recall@10 gains rest on external benchmark comparisons rather than internal redefinitions, making the work self-contained against independent data.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The approach rests on standard assumptions about the utility of pre-trained multimodal models and graph propagation rather than new invented entities or heavily fitted parameters beyond typical ML hyperparameters.

free parameters (1)
  • stage-specific hyperparameters
    Learning rates, embedding dimensions, and contrastive loss weights are expected but not enumerated in the abstract.
axioms (2)
  • domain assumption Foundation models with multimodal comprehension can meaningfully enhance raw recipe features
    Invoked as the basis for the first content-based enhancement stage.
  • domain assumption Message propagation over user-recipe interactions improves embedding quality
    Core premise of the second relation-based stage.

pith-pipeline@v0.9.0 · 5426 in / 1240 out tokens · 40662 ms · 2026-05-17T06:13:14.988558+00:00 · methodology

discussion (0)

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Reference graph

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