Title: Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multimodal Recommendation

Spectral Modeling

Graph Learning
Abstract: Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user–item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with representation-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes SSR, short for Structured Spectral Reasoning, a novel multimodal recommender system which works in the frequency domain and adaptively utilizes the graph and multimodal spectral signals to solve known issues in the multimodal recommendation literature. \n\nUnlike similar previous approaches, SSR performs a four stages pipeline, involving: (i) the decomposition of graph and multimodal signals into spectral bands to obtain a more granular representation; (ii) a modulation of the signal's reliability with some random masking of the spectral bands, to make the model robust to spurious frequency components; (iii) the fusion of the decomposed spectral bands into one unique representation; (iv) the alignment of spectral bands and modalities through contrastive learning.\n\nTo begin with, the graph and multimodal spectral signals are decomposed into spectral bands, and for each node in the dataset, they are fused with a gated attention weight. \n\nSecond, a masking mechanism is applied to randomly perturb the spectral bands during the training and to make the model robust to spurious frequency components; this objective is obtained through invariant risk minimization, a loss component of the overall loss function where the normal and the counterfactual settings are supposed to be close in their learned representations. \n\nThen, the model introduces a Graph HyperSpectral Neural Operator (G-HSNO) which can work three axes, namely, frequency bands, modality types, and graph topology. In this respect, the authors adopt Canonical Polyadic (CP) decomposition, a low-rank factorization of the kernel tensor adopted in the G-HSNO operator to reduce the overfitting and the parameter complexity. \n\nFinally, a Spectral Contrastive Regularization (SCR) is introduced to perform a contrastive learning loss encouraging intra-band modality consistency but preserving, at the same time, inter-band separation.\n\nExtensive experimental results on three popular datasets from the Amazon Reviews collection, against 14 state-of-the-art recommender and multimodal recommender systems demonstrate the efficacy of the proposed approach. Further analyses show the goodness of the approach also in cold-start scenarios. Then, an ablation study and a hyper-parameter sensitivity analyses prove the soundness of the architectural choices made by the authors. Finally, the authors show how effective is the framework at adapting at different decomposed frequencies across modalities, how well-aligned are the modalities (especially at the mid frequency), and how the adaptive spectral modulation can tailor different settings, such as cold- and non-cold-start items.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n$\\bullet$ The paper lays in one of the current hot topics in the literature of multimodal recommendation, namely, the translation of the task of multimodal recommendation to the frequency domain from the common spatial domain.   \n\n$\\bullet$ In relation to the first point, the authors clearly highlight the main limitations in current approaches from the literature (also recent ones which work in the spectral domain), and position the proposed approach within the state-of-the-art.  \n\n$\\bullet$ The methodology seems adequately sound and novel, where all main stages of the pipeline are clearly reported in the formalism and also in Figure 1. All theorems are adequately supported by the proofs as reported in the Supplementary Material.\n\n$\\bullet$ The experimental setting is quite extensive, with several analyzed dimensions which allow not only to show the efficacy of the proposed approach under overall performance perspectives, but also under specific evaluation dimensions which justify the effectiveness of each idea conveyed in the paper.\n\n**Weaknesses**\n\n$\\bullet$ To my understanding, the proposed framework could potentially be applied to many different scenarios where other side information is available apart from the simple collaborative signal. This is especially evident since the multimodal aspect appears only in Section 3.4 after the whole presentation of the methodology, which appears quite agnostic of the modalities. While this could indeed be a positive aspect of the approach (i.e., to be side-information agnostic), do the authors think that maybe the approach would have suited better to other scenarios? Something like knowledge graphs information in recommendation? And, in case, why did they choose to test it with multimodal recommendation?\n\n$\\bullet$ From my perspective, I see how the proposed approach has something in common with previous approaches in (multimodal) recommendation, such as DGCF [\\*] and GRCN. Specifically, the two mentioned approaches work by disentangling the embedding representation of nodes (and modalities) into aspects, which are learned end-to-end in the downstream task. To me, this appears as a spatial version of the frequency disentanglement of the spectral bands. Can the authors elaborate a little bit on that? How effective is translating the whole setting to the spectral domain instead of keeping it limited to the spatial one?\n\n$\\bullet$ Regarding the kernel tensor decomposition, the literature outlines a wide range of possible solutions apart from CP. For instance, another possible technique is Tucker decomposition, which usually allows more flexibility than CP. Why did the authors go for CP, and did they try other solutions before coming up with CP? I think this aspect should be better clarified. A similar consideration could be done for the semantic-aware frequency fusion. Why did the authors use gated attention? What are the specific benefits of it, and did they try with other similar solutions? This aspect should also be better clarified. \n\n$\\bullet$ The code is not released at review time, thus not ensuring complete reproducibility of the approach. While I acknowledge the usage of the framework MMRec for all the used baselines, I think the work would benefit from sharing the code also of the proposed approach.\n\n**References**\n\n[\\*] Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua: Disentangled Graph Collaborative Filtering. SIGIR 2020: 1001-1010\n\nQUESTIONS:\nMy questions are mainly related to some of the weaknesses I pointed out above.\n\nQ1) Do the authors think that the proposed framework would have suited better to other scenarios than multimodal recommendation? For instance, knowledge graphs information in recommendation? And, in case, why did they choose to test it with multimodal recommendation?\n\nQ2) How different is the proposed approach from similar solutions which perform disentanglement in the spatial domain? What is the benefit of performing it in the frequency domain?\n\nQ3) Why did the authors go for CP in the kernel tensor decomposition, and did they try other solutions before coming up with CP?\n\nQ4) Why did the authors use gated attention in the semantic fusion of spectral bands? What are the specific benefits of it, and did they try with other similar solutions?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes Structured Spectral Reasoning (SSR), a unified framework for multimodal recommendation that decomposes user-item graph signals into frequency bands, applies spectral causal masking to suppress unreliable components, and fuses them via a novel Graph HyperSpectral Neural Operator combined with contrastive regularization. The approach enables adaptive, frequency-aware representation learning, achieving state-of-the-art performance on multiple Amazon benchmarks, especially under sparse and cold-start conditions.

STRENGTHS AND WEAKNESSES:
Pros:
1. Clear writing and easy to read.
2. The method is reasonable and the experiment shows the effectiveness of the method.
3. Provides theoretical support.

Cons:
1. The method is complex and difficult to apply. The data support for complexity analysis is limited and not intuitive.
2. The experiments are more about showing the overall performance and analyzing the method, and the improvements proposed in the motivation are not experimentally verified.
3. Some analysis experiments only have the proposed method but lack baseline comparison, which is not intuitive enough.
4. The author does not discuss limitations, and there is no clear enough discussion of limitations in the appendix chapter corresponding to the Checklist.

QUESTIONS:
1. Could the authors provide more evidence to demonstrate the scalability of the method?
2. Could the authors design targeted experiments to explicitly verify that the proposed frequency decomposition and causal masking improve robustness to modality-specific noise or spurious correlations?
3. Could the authors provide concrete examples or user studies that validate this interpretability?

LIMITATIONS:
Refer to Weaknesses and Questions
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

comments: SUMMARY:
This paper presents a framework called SSR (structured spectral reasoning) for multimodal recommendation which leverages frequency domain to overcome problems of modality-specific noise, semantic inconsistency, and unstable propagation over user-item graphs. This is done by operating in the frequency domain, by using a 4 stage pipeline: a) decomposition (multimodal signals transformed into frequency bands), b) modulation - casually inspired masking technique used to regularize model, c) fusion where a fusion is done across different frequency bands and finally alignment - where different representations are aligned in the same band using a special contrastive loss. The efficacy of the proposed method is verified on 3 benchmark datasets, where SSR outperforms strong baselines, particularly in sparse and cold start settings.

STRENGTHS AND WEAKNESSES:
Strengths:
Overall, the paper is very well presented, concisely written and easy to follow. In the context of multimodal handling, the SCM piece is particularly interesting when IRM is used to identify invariant predictors under spectral perturbations. The experimental results look promising. The detailed ablation is appreciated. 

Weaknesses:
1. The method depends on GFT which can be computationally prohibitive as a preprocessing step, how is that handled?  
2. What is the motivation behind choosing equal energy bands?

QUESTIONS:
See weaknesses above.

LIMITATIONS:
Not mentioned, see questions above.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a unified framework, termed SSR, for frequency-aware multimodal recommendation. It follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands to isolate semantic granularity; (ii) Modulate signal reliability through spectral causal masking; (iii) Fuse complementary frequency cues using hyperspectral reasoning; and (iv) Align modality-specific spectral features via contrastive regularization. Extensive experiments demonstrates the effectiveness of SSR on under sparse and cold-start scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation and the theoretical analysis are well described in Methodology section and the proposed SSR pipeline is clear with four diverse steps.\n2. The experimental results consistently demonstrate SSR’s superior performance compared to many baselines, with particularly strong improvements in cold-start and sparse secnarios.\n3. The parameter robustness and generalizability of SSR have been well-verified in section 4.\n\nWeaknesses:\n1. The overall structure is not very clear to understand. For example, the Decomposition module does not explicitly state that the three frequency-specific representations are derived from the same modality, which may lead to misunderstandings.\n2. Cross-modality alignment is one of the core contribution in this paper, however, it  doesnot cite some multimodal recommendation studies that explicitly focus on cross-modal learning.\n3. The validation datasets all comes from Amazon platform, which may introduce bias in performance comparison.\n4. There is an inconsistency between Figures 3 and 4: in Figure 3, the cross-modality correlation among the three modalities in the High Frequency component appears lower, whereas in Figure 4 it is the highest. This discrepancy is not clearly explained in Section 4.6 or Appendix F.3.\n5. The authors donot provide the significance analysis in performance comparison, and the corresponding code and dataset have not been open-sourced.\n6. Figure 4 in the manuscript is not referenced or discussed, and it is identical to Figure 5 in the Appendix.\n\nQUESTIONS:\n1. Why the discovery is not align in Figure 3 and Figure 4? Or anything I misunderstand.\n2. The figures in this paper should be further polished to enable potential readers to understand the overall structure.\n3. Please cite some cross-modal recommendation algorithms to illustrate your superior.\n4. Beyond the time complexity analysis, it would be helpful to include a comparison of the actual training and inference runtimes between SSR and several traditional or advanced baselines.\n\nLIMITATIONS:\nPlease refer to limitations and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}