Diff-MSIN: Diffusion-based Multi-modal CTR Framework

Updated 9 July 2026

The paper introduces Diff-MSIN, a framework that augments traditional CTR models by explicitly modeling modality-specific, shared, and synergistic features across ID, text, and image inputs.
It employs a denoising-style diffusion process that iteratively refines multimodal signals via Gaussian noise injection and cross-modal attention, optimizing feature synergy.
Integration with standard CTR backbones demonstrates a consistent AUC improvement (at least 1.67%) and highlights its adaptability in complex multi-modal recommendation settings.

Diffusion-based Multi-modal Synergy Interest Network (Diff-MSIN) is a click-through rate (CTR) prediction framework for multi-modal sequential recommendation that augments an existing sequence CTR backbone with explicit modeling of modality-specific, shared, and synergistic information across ID, text, and image modalities. In the formulation described in "Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction" (Cui et al., 29 Aug 2025), Diff-MSIN is designed for settings in which user profile features, historical behavior sequences, and target-item features are jointly available, and in which ID-only CTR models are considered insufficient for comprehensively modeling users’ multi-modal preferences.

1. Definition, problem setting, and design rationale

Diff-MSIN is proposed for CTR prediction with user behavior sequences, where the task is to estimate whether a user will click a target item given user profile features, the user’s historical behavior sequence, and the target item’s features (Cui et al., 29 Aug 2025). The framework operates in a multi-modal CTR setting in which the user’s history and the target item contain three principal modalities: ID modality, text modality, and image modality.

The motivating claim is that standard CTR methods mainly rely on ID-based features such as item ID, category ID, brand ID, seller ID, and user ID, and therefore cannot comprehensively model the actual causes of user clicks. The paper frames this limitation in terms of two deficiencies of prior multimodal fusion methods. First, they may fail to disentangle commonality and specificity across modalities, which leads to redundant encoding of overlapping information and weak robustness under conflicting signals. Second, they may fail to model synergistic effects between modalities, even when click behavior depends on cross-modal combinations rather than on any single modality in isolation.

Within this formulation, Diff-MSIN distinguishes three forms of information. Specific information is modality-dependent and is extracted by modality-specific experts. Common or shared information is intended to capture semantics repeated across modalities. Synergistic information is defined as information emerging from interaction between modalities rather than from direct summation or concatenation. This distinction is structurally central: the framework does not treat multimodal fusion as a single aggregation step, but as a staged process of extraction, decoupling, interaction, and adaptive fusion.

A common misconception is that the phrase “diffusion-based” implies a full generative diffusion model that synthesizes items or recommendation outputs. In Diff-MSIN, that is not the case. The paper explicitly uses “diffusion-based” to denote a denoising-style iterative cross-modal interaction mechanism inside the Synergistic Relationship Capture module. Forward diffusion injects Gaussian noise into modality representations, and reverse diffusion uses cross-modal interactions to reconstruct or refine those representations (Cui et al., 29 Aug 2025).

2. Overall framework and information flow

Diff-MSIN is presented as a plug-in framework that can be embedded into an existing sequence CTR model such as DIN, ETA, TWIN, or DPN, with the main presentation using DIN as backbone (Cui et al., 29 Aug 2025). The full architecture contains feature extraction, user interest modeling for text and image sequences, the Multi-modal Feature Enhancement (MFE) module, a Knowledge Decoupling method, the Synergistic Relationship Capture (SRC) module, the Feature Dynamic Adaptive Fusion (FDAF) module, and the final CTR prediction stage.

The input structure includes user profile features, a historical sequence

$S=\{S^{id}, S^{m1}, S^{m2}, \ldots, S^{mn}\},$

and a target item

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$

In practice, the historical sequence mainly consists of $S^{id}$ , $S^{te}$ , and $S^{im}$ . Text and image are encoded with pretrained encoders, defaulting to CLIP, and converted into dense vectors together with the corresponding target text and target image embeddings.

User interest modeling for text and image is target-aware. For each modality, the framework computes attention over historical behaviors conditioned on the corresponding target modality, producing weighted sequence representations that are then summed into fixed-length interest vectors $E_a^{te}$ and $E_a^{im}$ . The ID modality is handled by the backbone CTR model, for example DIN. This means that multimodal interest modeling in Diff-MSIN is sequential and target-aware before any cross-modal enhancement is applied.

The subsequent pipeline is hierarchical. MFE extracts modality-specific and common knowledge. Knowledge Decoupling regularizes those spaces so that specific experts remain distinguishable while shared representations are aligned. SRC then performs multi-step cross-modal interaction with forward noise injection and reverse denoising, producing a synergistic representation $E_{\text{syn}}$ . FDAF finally treats the ID feature as primary and the multimodal outputs as auxiliary, using target-ID-conditioned weighting, CrossNet-based interaction among auxiliary features, and attention-based non-intrusive fusion into the ID feature. The resulting representation is passed to the base CTR output head.

This architecture places Diff-MSIN within a broader trend in diffusion-assisted recommendation, but with a specific emphasis on cross-modal synergy rather than latent-interest denoising alone. By comparison, DMI refines coarse multi-interest vectors at the representation-dimension level after conventional multi-interest extraction (Le et al., 8 Feb 2025), whereas Diff-MSIN uses diffusion-style denoising to model interaction among modalities in a CTR pipeline rather than to clean a single interest embedding.

3. Core modules

The MFE module is designed to extract specific information from each modality, common or shared information across modalities, and improved modality representations for subsequent synergy modeling (Cui et al., 29 Aug 2025). Its inputs are the text interest representation $E_a^{te}$ , the image interest representation $E_a^{im}$ , and the ID representation from the embedded CTR backbone.

Each modality has a specific expert: $s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 0

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 1

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 2

In parallel, each modality also passes through a shared expert: $s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 3

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 4

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 5

and the shared outputs are averaged to form

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 6

A gate then mixes specific and shared information for each modality: $s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 7

$s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 8

The intended effect is to prevent direct fusion from entangling overlapping and unique information too early.

3.2 Knowledge Decoupling

Knowledge Decoupling is the paper’s explicit mechanism for forcing different knowledge domains to remain distinguishable. The loss is written using cosine similarity between expert outputs: $s^t=\{s^{t_{id}}, s^{t_{te}}, s^{t_{im}}\}.$ 9 and

$S^{id}$ 0

with $S^{id}$ 1.

Minimizing this objective reduces similarity among modality-specific expert outputs while encouraging similarity among shared representations. The paper notes an inconsistency here: the prose mentions “five tensors” and includes $S^{id}$ 2, while the equation sums over only three modalities. Accordingly, the exact implementation scope of the decoupling loss is underspecified.

3.3 Synergistic Relationship Capture

The SRC module is the component that gives Diff-MSIN its “diffusion-based” characterization. It takes the modality expert features as initial states,

$S^{id}$ 3

and performs multi-step synergistic feature interaction over $S^{id}$ 4 time steps (Cui et al., 29 Aug 2025).

The forward diffusion step injects Gaussian noise: $S^{id}$ 5 where $S^{id}$ 6. Reverse denoising is driven by cross-modal interaction: $S^{id}$ 7

$S^{id}$ 8

followed by the reverse update

$S^{id}$ 9

After the reverse process, the denoised modality states are fused into

$S^{te}$ 0

The paper interprets this process as progressive synergy modeling rather than one-shot fusion. A plausible implication is that the design treats cross-modal interaction as an iterative refinement problem: one modality may help denoise another, and synergy is formed only after repeated interaction steps.

3.4 Feature Dynamic Adaptive Fusion

FDAF is designed to adaptively weight modalities according to target and user context while preserving ID as the primary feature (Cui et al., 29 Aug 2025). The module computes target-ID-conditioned weights

$S^{te}$ 1

and forms weighted auxiliary features

$S^{te}$ 2

$S^{te}$ 3

The notation for $S^{te}$ 4 is reused on both sides in the paper, and the intended meaning is a weighted synergistic feature.

These auxiliary features are concatenated,

$S^{te}$ 5

and passed through a CrossNet: $S^{te}$ 6 Finally, non-intrusive fusion with the primary ID feature is performed by

$S^{te}$ 7

This design reflects the paper’s stated fusion philosophy: multimodal information should guide or refine the ID feature rather than overwrite it through direct concatenation.

4. Mathematical formulation and optimization

Diff-MSIN encodes text and image sequences and targets as

$S^{te}$ 8

$S^{te}$ 9

followed by target-aware attention

$S^{im}$ 0

and sum pooling

$S^{im}$ 1

These vectors feed the expert and shared-expert branches already described.

The main CTR objective is binary cross-entropy: $S^{im}$ 2 The synergy supervision term is

$S^{im}$ 3

where $S^{im}$ 4 is the click label. The paper states that clicked samples are pulled closer to the target representation and non-clicked samples are pushed away, but it does not clearly define exactly what $S^{im}$ 5 denotes. This is one of the framework’s explicit underspecifications.

The full loss is

$S^{im}$ 6

The optimization details reported are: PyTorch implementation, mini-batch size $S^{im}$ 7, learning-rate search over $S^{im}$ 8, hidden-size search over $S^{im}$ 9, expert feature dimension $E_a^{te}$ 0, expert networks implemented as separate MLPs with hidden size $E_a^{te}$ 1 and ReLU, gate network implemented as a single-layer MLP plus sigmoid, and early stopping if AUC does not improve for 10 consecutive epochs (Cui et al., 29 Aug 2025).

For the diffusion-style part, the implementation section reports a maximum time step $E_a^{te}$ 2, a linear noise schedule $E_a^{te}$ 3 from $E_a^{te}$ 4 to $E_a^{te}$ 5, Gaussian noise $E_a^{te}$ 6, and cross-modal interaction $E_a^{te}$ 7 implemented with multi-head attention using 8 heads and hidden size $E_a^{te}$ 8. The paper later reports best performance around $E_a^{te}$ 9, and explicitly notes inconsistency between the implementation section and the parameter analysis. This discrepancy is part of the published description rather than an external criticism.

5. Empirical profile

The empirical evaluation uses four datasets: Rec-Tmall, Amazon Home, Amazon Clothing, and Amazon Arts (Cui et al., 29 Aug 2025). Their reported statistics are as follows.

Dataset	Users	Items	Interactions
Home	31,387	64,302	296,428
Clothing	64,183	134,064	614,601
Arts	23,592	16,340	264,801
Rec-Tmall	72,051	93,466	328,387

The sequence preprocessing retains users with behavior length at least 5 and keeps the 50 most recent historical records. Evaluation uses AUC and RelaImpr. Baselines include LR, FM, DeepFM, YoutubeNet, DIN, and multimodal or related methods such as LMF, MTFN, NAML, MARN, GMMF, MAKE, EM3, QARM, and SimCEN. The framework is also evaluated as a plug-in on DIN, DPN, ETA, and TWIN.

In the main AUC table, Diff-MSIN reports $E_a^{im}$ 0 on Rec-Tmall, $E_a^{im}$ 1 on Home, $E_a^{im}$ 2 on Clothing, and $E_a^{im}$ 3 on Arts, exceeding the listed strong baselines on all four datasets. The paper states that the approach yields a significant improvement of at least $E_a^{im}$ 4 compared to the baseline. It also reports consistent gains when integrated with multiple sequence CTR backbones, which supports the claim that Diff-MSIN is a general plug-in multimodal interest enhancement framework rather than a backbone-specific design.

Ablation results remove MFE and SRC, FDAF, SRC alone, or all three major modules. The surrounding text interprets these results as evidence that MFE and SRC contribute significantly by modeling specific, common, and synergistic information, and that FDAF reduces fusion noise while modeling user modality preferences. The paper itself notes inconsistency in the Arts column, where some ablated settings appear numerically higher than the full model despite the textual conclusion that the full model is best. This inconsistency should therefore be treated as a reporting issue in the experimental presentation.

The sensitivity analysis reports that removing either text or image decreases AUC, that removing modality-specific, common, or synergistic features degrades performance, and that moderate diffusion depth is preferable: $E_a^{im}$ 5 is described as insufficient for cross-modal interaction, $E_a^{im}$ 6 as overfitting, and $E_a^{im}$ 7 as best, with AUC $E_a^{im}$ 8 in the cited analysis. On Rec-Tmall, the efficiency comparison reports training time per epoch of $E_a^{im}$ 9 seconds and inference time per prediction of $E_{\text{syn}}$ 0 seconds for Diff-MSIN, making it slower than SimCEN, EM3, and MAKE but still presented as practically usable.

6. Position within diffusion-based multimodal recommendation

Diff-MSIN belongs to a broader set of 2024–2025 works that use diffusion processes not only for generation but also for denoising, graph construction, or representation refinement in recommender systems. Its closest conceptual link in the provided literature is M $E_{\text{syn}}$ 1BSR, which studies multi-modal, multi-behavior sequential recommendation with conditional diffusion-based feature denoising and a Multi-Expert Interest Extraction layer (Cui et al., 7 Aug 2025). Both frameworks combine multimodal inputs, diffusion-style denoising, and explicit decomposition of shared and specific signals. The difference is that Diff-MSIN is formulated for CTR prediction with ID-primary non-intrusive fusion, whereas M $E_{\text{syn}}$ 2BSR focuses on multi-behavior sequential recommendation with click and favor as shallow and deep behaviors.

DMI is also relevant because it demonstrates that diffusion in recommendation can act as a latent interest refiner rather than an item generator (Le et al., 8 Feb 2025). Diff-MSIN shares the denoising orientation, but the object being refined is different: DMI operates on coarse interest vectors, whereas Diff-MSIN applies diffusion-style noise injection and reverse interaction to modality expert states. DiffMM offers a separate line of comparison by using Gaussian diffusion over user-item interaction vectors to generate modality-aware user-item graphs for sparse recommendation (Jiang et al., 2024). In that sense, DiffMM addresses modality-aware relational structure, while Diff-MSIN addresses modality-specific, shared, and synergistic knowledge inside a sequence CTR architecture. At a more general multimodal generative level, Multi-modal Latent Diffusion proposes deterministic unimodal encoders followed by masked diffusion over concatenated latents to support arbitrary-subset conditioning (Bounoua et al., 2023); this suggests a different multimodal diffusion design space from the one adopted in Diff-MSIN.

Several limitations and ambiguities are explicit in the Diff-MSIN description itself. The definition of $E_{\text{syn}}$ 3 in the synergy loss is unclear. The scope of Knowledge Decoupling is inconsistent between prose and equation. The time-step setting $E_{\text{syn}}$ 4 is not fully consistent between implementation notes and parameter analysis. The framework assumes image and text availability and does not extensively evaluate severe modality absence. It also adopts the assumption that ID should remain the primary feature, which is appropriate for CTR as framed in the paper but may be less natural in cold-start regimes where content should dominate.

Taken together, Diff-MSIN can be characterized as a multimodal CTR framework whose distinctive contribution lies not in adding image and text features per se, but in structuring multimodal modeling into four coupled operations: extraction of common and specific information through MFE, separation through Knowledge Decoupling, iterative cross-modal denoising and synergy formation through SRC, and target-conditioned non-intrusive fusion through FDAF (Cui et al., 29 Aug 2025). This suggests that, within diffusion-based recommendation research, the framework is best understood as a specialized architecture for multimodal synergy-aware CTR prediction rather than as a general-purpose generative diffusion recommender.