Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios

Chanyoung Park; Hongseok Kang; Jiwan Kim; Kibum Kim; Sein Kim

REVIEW 3 major objections 4 minor 44 references

Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read DGMRec claims that missing modality features in recommender systems should be generated, not injected, and that disentangling each modality into shared and unique parts makes this generation accurate enough to beat existing multimodal…

desk verdict DGMRec is a genuinely integrated method for missing-modality recommendation, but the new-item experiments are unexplained by the mechanism as written and need clarification before the headline claim holds. read the letter →

arxiv 2504.16352 v1 pith:K2S3IDZA submitted 2025-04-23 cs.IR cs.AI

Jiwan Kim , Hongseok Kang , Sein Kim , Kibum Kim , Chanyoung Park This is my paper · ORCID

classification cs.IRcs.AI

keywords Multi-modalrecommendationMissingmodalitiesFeaturedisentanglementCollaborativefilteringModalitygenerationCross-modalretrievalMutualinformationGraphrefinement

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that the right way to handle missing modalities in recommender systems is to generate the missing features rather than to inject crude substitutes or ignore them. It proposes DGMRec, which splits each modality's feature vector into a general part shared across modalities and a specific part unique to that modality, then reconstructs missing modality features by combining aligned general features from available modalities with user modality preferences. The claim is that this generation-based approach yields consistently better ranking quality than existing multimodal and missing-modality-aware recommenders, including when all modalities of an item are missing and when new items appear at test time. It also enables cross-modal retrieval, a task that existing multimodal recommenders cannot perform at all.

What carries the argument

The central object is the disentangled modality representation: each modality $m$ is passed through a shared general encoder $f_g$ and a modality-specific encoder $f^s_m$ to yield general features $E^g_m$ and specific features $E^s_m$. Two information-based losses drive the separation: the Contrastive Log-ratio Upper Bound (CLUB) minimizes mutual information between general and specific features within the same modality, while InfoNCE maximizes mutual information between general features across modalities. For missing modalities, a general generator $G^g_m$ maps concatenated aligned general features from other modalities to the missing general feature, and a specific generator $G^s_m$ maps the item's user-modality preference embedding $P_{i,m}$ (the average of preference embeddings of users who interacted with the item) to the missing specific feature; a per-modality decoder then reconstructs the raw feature, and the generated features refine the item-item graph with an adaptive blend of old and new adjacency matrices.

What would settle it

Take items whose modalities are fully available, artificially mask their image and text features entirely, and compare DGMRec's generated features for those items against the true held-out features using cosine similarity or MSE, against a baseline that uses the global mean or nearest-neighbor features; if the generated features are no closer to the truth than the baseline, the mechanism behind the reported gains does not work as described. A second check is to replace the user-modality preference embedding $P_{i,m}$ in Eq. (13) with random vectors of the same dimension; if recommendation performance does not drop substantially, then the specific-feature generator is not actually using item content information.

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Extended reading notes

Core claim

On its own terms, the paper establishes that a recommender that explicitly separates modality features into general and specific components, and generates missing components rather than injecting average or nearest-neighbor features, outperforms state-of-the-art multimodal recommenders and missing-modality-aware recommenders in missing-modality settings, new-item settings, varying missing ratios, and extreme cases where no modality remains. The key result is that specific features can be generated from user modality preference embeddings alone, so even an item with every modality missing can still receive a useful modality representation. The paper reports consistent improvements over the best baseline across four datasets in both evaluation settings, with relative Recall@20 gains ranging from roughly 4.7% to 14.1%, and it shows the model can retrieve similar items through generated features when all modalities are absent.

Load-bearing premise

The load-bearing premise is that the average of the modality-preference embeddings of the users who interacted with an item contains enough item-specific information to regenerate that item's unique modality attributes, so that even an item with no available modalities can receive a faithful modality representation.

Editorial extensions

If this is right

Recommenders that generate missing modalities retain more ranking quality as the missing ratio grows; the paper reports a relative performance drop of 10.2% at 80% missing modality, compared with 15.1% for the best baseline.
New items with partial or no modality data can still be represented through generated features, directly addressing the cold-start item recommendation problem.
Cross-modal retrieval becomes possible: given a query item with a missing modality, generated features allow retrieving similar items even when every modality is absent.
Disentangling modality features into shared and unique parts improves the quality of item representations for ranking, beyond the benefit of generation alone.
Modality knowledge and collaborative knowledge reinforce each other through alignment losses, so the generated features contribute to the recommendation score rather than remaining isolated content representations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the preference-embedding generation works as claimed, the same user-modality preference embeddings could serve as a portable representation for cold-start users, transferring modality preferences across domains without retraining.
The directed-edge graph refinement strategy, which only adds edges from available modalities to missing ones, suggests a general recipe for any graph-based recommender to ingest generated features without contaminating original signals.
A direct testable extension is to hide an available modality at test time and check whether DGMRec's generated feature for that modality approximates the true feature better than nearest-neighbor injection; if it does not, the ranking gains must come from the alignment or disentanglement losses rather than from faithful generation.
The disentanglement losses could transfer to other multimodal tasks, such as retrieval with mixed text-and-image queries, wherever modality-specific detail matters.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, the paper does something real: it separates shared and modality-specific information using mutual information bounds (CLUB and InfoNCE), then generates missing modalities with decoders. That combination in one recommend system is new to me, and the ablations say each piece contributes. The gains over strong baselines on the Amazon and TikTok benchmarks are roughly 5-11% in recall/NDCG, which is in the range practitioners care about. Second, there is a load-bearing hole in the new-item setting. For items that appear only in the test set, the interacted-user set N_i is empty, so Eq. 2 and Eq. 13 are undefined. The paper does not say what the code substitutes. If it uses a zero or constant vector, the generated specific features are item-independent, so the mechanism cannot explain the larger relative gains in the right half of Table 2. If it uses test-time interactions to compute P_{i,m}, that is a leak. Either way, the authors need to specify what is implemented. This is not a cosmetic issue: the new-item scenario is a core part of the practical claim. Other soft spots are more routine: no standard deviations or significance tests, so the word "consistently" is stronger than the evidence; at 0% missing ratio DGMRec actually trails GUME slightly in Figure 4(a); and the TikTok raw features are not released, so that part cannot be independently verified. The code is available but without a commit hash, so replication is partial. None of this makes me think the method is fake. The plain missing-modality setting, where items have training interactions, is well-defined and the gains there are credible. The disentanglement visualization and parameter sensitivity analysis are also encouraging. The cross-modal retrieval result is a nice byproduct, though it would be stronger with a second content-based baseline. Bottom line: this paper deserves a serious referee. I would send it to review, asking the authors to clarify the new-item protocol, add variance estimates, and temper the "consistently" wording. If the new-item mechanism turns out to be leaky, the contribution shrinks considerably; if it is just under-specified, it is a fixable reporting gap.

Referee Report

3 major / 4 minor

Summary. The paper proposes DGMRec, a multimodal recommender system designed for missing-modality scenarios. The model splits each modality feature into a general component shared across modalities and a specific component unique to the modality, using CLUB and InfoNCE losses to enforce disentanglement and cross-modal alignment. Missing modality features are generated by a reconstruction-based module: general features are produced from other available modalities, while specific features are generated from an item-level aggregation of user modality preference embeddings (Eq. 13). The generated features are used to refine an item-item graph, and two alignment losses connect modality representations with collaborative filtering. Experiments on Baby, Sports, Clothing, and TikTok compare DGMRec with CF, MRS, and MMA-RS baselines in two settings (missing modality and missing modality plus new items), together with ablations, missing-ratio analysis, sensitivity analysis, time complexity, and cross-modal retrieval.

Significance. If the empirical claims are reliable, the paper makes a useful contribution: it offers a generation-based alternative to injection-based and robustness-based handling of missing modalities, reports directionally consistent gains over a wide set of baselines, and enables a cross-modal retrieval task that injection methods cannot support. The release of code, the broad baseline coverage, and the inclusion of ablations and sensitivity analyses are strengths. The main caveats are that the reported numbers carry no variance information and that the mechanism underlying the new-item results is under-specified; as written, the claimed new-item gains cannot be traced to the generation module without additional clarification.

major comments (3)

[§3.1, Eq. (2); §3.3.2, Eq. (13); §4.1] For the "Missing Modality + New Items" setting, p_{i,m} is undefined for new items. New items are defined in Section 4.1 as appearing only in the test set, so their neighbor set N_i is empty when the training split is used, and the manuscript does not specify any default for P_{i,m} in this case. If P_{i,m} is computed from test interactions, that would be label leakage; if it is replaced by a constant or omitted, the specific-feature generator G^s_m(P_{i,m}) produces an item-independent vector, and the generation mechanism cannot explain the large new-item gains in Table 2 (e.g., Baby R@20 +14.06%). Please clarify how P_{i,m} is obtained for new items and report results on the new-item subset separately, because the current text makes the central new-item claim untestable.
[§3.3.2; §4.2.2] The claim that specific features generated via the preference-based approach remain effective when no modality is available is not supported for items with no interactions. When all modalities are missing, the general-feature generator in Eq. (12) receives only mean-imputed inputs and outputs a constant; when the item is also new, the specific-feature generator in Eq. (13) is constant by the argument above. The only item-dependent signal left is a randomly initialized ID embedding that was not trained for new items. The paper should either show that P_{i,m} is well-defined and trained for such items or provide an analysis restricted to items that are simultaneously new and have zero available modalities; otherwise the reported gains in this cell cannot be attributed to the proposed generation module.
[Tables 2-4; Figures 3-4] No standard deviations, confidence intervals, or significance tests are reported. The central claim is an empirical superiority claim (Section 4.2.1), and several reported improvements are small (e.g., TikTok R@20 +4.69% in the missing-modality setting). Without any uncertainty quantification, the reader cannot distinguish a systematic advantage from run-to-run variation. Please add multiple-seed results with standard deviations and, where appropriate, paired significance tests.

minor comments (4)

[§4.2.3] The text says "Across all missing ratios, DGMRec consistently outperformed all baselines, except for the 0% baseline," which is internally contradictory; please rephrase and discuss the 0% case explicitly.
[Table 3] The cross-modal retrieval setup is under-specified: Hit@10 and Hit@20 are not defined for the retrieval task, and the NN baseline is absent for the two-missing-modalities case, so the comparison is incomplete.
[Abstract and §1] There are several typographical issues, including "Disentangling and Generating Modality Recommender" without spacing, "TikTook" instead of "TikTok", and an unusual denominator symbol in Eq. (8) that should be a summation sign.
[§4.1] The TikTok dataset's raw features are not publicly available; this is disclosed, but the resulting reproducibility limitation should be addressed explicitly in the experimental section, for example by noting which downloaded feature files are required to run the released code.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DGMRec's central claims are empirical benchmark comparisons, not derived predictions.

full rationale

The paper's strongest claim is an empirical performance comparison in Table 2: DGMRec consistently outperforms existing MRSs and MMA-RSs on standard benchmarks under missing-modality and new-item settings. No fitted constant, learned parameter, or calibration curve is presented as a theoretically predicted quantity, so the core evaluation is not a derivation from its own inputs. The generation mechanism (Eqs. 12-15) is trained with reconstruction and generation losses on items whose modalities are available, and its effect is then measured on held-out ranking performance; this is a standard train/predict loop, not a self-definitional reduction. The only same-group citation is reference [10], used for the generic statement that modality features lack collaborative knowledge. That claim is used to motivate the alignment losses, but the paper independently demonstrates via ablation (Table 4, 'w/o Alignment') that these losses contribute to performance, so the self-citation is not load-bearing. No uniqueness theorem from the authors' prior work is invoked, and no ansatz is smuggled in solely through a citation. The most concerning potential issue, that Eq. 13 generates specific features from P_{i,m} computed over N_i while new items may have empty N_i, is a correctness or possible label-leak question rather than a circularity: the paper does not state that test interactions are used to compute P_{i,m}, and under the no-speculation rule this cannot be scored as circular from the text alone. Overall, the derivation chain is self-contained with respect to the empirical claims, and no circular step is exhibited.

Assumptions & free parameters 5 free parameters · 6 assumptions · 3 invented entities

The central claim depends mainly on domain assumptions about generation from preferences and shared semantic spaces, not on exotic theoretical axioms. All invented entities are internal latent variables whose evidence is entirely in-paper (ablations, visualization, and the cross-modal retrieval experiment).

free parameters (5)

lambda_1 = 0.01 (best on Baby/Clothing)
Weight for the disentanglement loss L_disentangle, tuned from {1.0, 1e-1, 1e-2, 1e-3}.
lambda_2 = 0.01
Weight for the alignment loss L_align, tuned from the same set; reported as the most sensitive hyperparameter.
alpha = moderate values, not extremes
Interpolation weight for graph refinement in Eq 16, tuned from [0,1] with interval 0.2.
modality_generation_interval = 5 epochs
How often generated features refresh the item-item graph, fixed by the authors.
temperature_tau = not reported
Contrastive temperature in the InfoNCE loss, tuned from [0,1] but the selected value is not stated.

assumptions (6)

standard math CLUB loss (Eq 7) upper-bounds mutual information and minimizing it reduces dependence between general and specific features.
Taken from Cheng et al. [2]; assumed valid for the variational q_phi used.
standard math InfoNCE loss (Eq 8) provides a lower bound on mutual information whose maximization aligns general features across modalities.
Taken from Oord et al. [15]; standard contrastive learning assumption.
domain assumption General features of different modalities lie in a shared semantic space, so concatenating available ones and passing through a 2-layer MLP can generate a missing modality's general features (Eq 12).
Core generative assumption of the Missing Modality Generation module; not proven, only ablated.
domain assumption The average of interacting users' modality preference embeddings represents an item's modality-specific content (Eqs 2 and 13).
Load-bearing for specific-feature generation when other modalities are absent; no independent evidence is provided.
domain assumption Synthetic missingness, with uniform random selection of which modality is missing and an even split over missing counts, approximates real missing-modality patterns.
Experimental design in Section 4.1; not validated against natural missingness.
domain assumption Pretrained modality features (CNN, SBERT, TikTok model) are valid fixed inputs.
Standard in multimodal recommender literature; TikTok features are not public.

invented entities (3)

General modality feature (E^g_m)
purpose: Captures modality-shared item semantics and is aligned across modalities via InfoNCE.
No external falsifiable handle; evidence is internal ablations, TSNE, and similarity trends.
Specific modality feature (E^s_m)
purpose: Captures modality-unique item attributes to preserve distinct modality information.
Same as above; internal evidence only.
User modality preference embedding (P_{u,m})
purpose: Models a user's tendency to favor particular modality content; aggregated into item preference P_{i,m} for specific-feature generation.
A new learned embedding matrix; no independent evidence outside the paper's tasks.

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Cite this review

Pith. "Pith review of Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios." pith.science (2026). https://pith.science/paper/K2S3IDZA

@misc{pith2026250416352,
  author       = {Pith},
  title        = {Pith review of: Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K2S3IDZA}},
  note         = {Machine review of arXiv:2504.16352}
}

read the original abstract

Multi-modal recommender systems (MRSs) have achieved notable success in improving personalization by leveraging diverse modalities such as images, text, and audio. However, two key challenges remain insufficiently addressed: (1) Insufficient consideration of missing modality scenarios and (2) the overlooking of unique characteristics of modality features. These challenges result in significant performance degradation in realistic situations where modalities are missing. To address these issues, we propose Disentangling and Generating Modality Recommender (DGMRec), a novel framework tailored for missing modality scenarios. DGMRec disentangles modality features into general and specific modality features from an information-based perspective, enabling richer representations for recommendation. Building on this, it generates missing modality features by integrating aligned features from other modalities and leveraging user modality preferences. Extensive experiments show that DGMRec consistently outperforms state-of-the-art MRSs in challenging scenarios, including missing modalities and new item settings as well as diverse missing ratios and varying levels of missing modalities. Moreover, DGMRec's generation-based approach enables cross-modal retrieval, a task inapplicable for existing MRSs, highlighting its adaptability and potential for real-world applications. Our code is available at https://github.com/ptkjw1997/DGMRec.

Figures

Figures reproduced from arXiv: 2504.16352 by the authors.

**Figure 2.** Overview of DGMRec framework. It consists of the Disentangling Modality Feature module and the Missing Modality Generation module. In the Missing Modality Generation module, we illustrate the case where an item is associated with the text modality while the image modality is missing. 3.2 Disentangling Modality Feature Module Existing MRSs directly align modality features [6, 11] or combine adjacency views across mod… view at source ↗

**Figure 4.** (a) Performance on various missing ratios, and (b) [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: (a) Visualization of disentangled modality features [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Performance on different hyper-parameters [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, and Meng Wang. 2024. Multimodality invariant learning for multimedia-based new item recommendation. In Proceedings of the 47th Inter- national ACM SIGIR Conference on Research and Development in Information Retrieval. 677–686

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