iFusion: Integrating Dynamic Interest Streams via Diffusion Model for Click-Through Rate Prediction
Authors: Ziheng Ni
Congcong Liu
Yuying Chen
Zhiwei Fang
Changping Peng
Zhangang Lin
Ching Law
Jingping Shao
Venue: ICLR 2026 Poster (2026)
Keywords: User Behavior Modeling
Diffusion Models
Dynamic Interest Fusion
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Click-through rate (CTR) prediction is crucial for recommendation systems and online advertising, relying heavily on effective user behavior modeling. While existing methods separately refine long-term and short-term interest representations, the fusion of these behaviors remains a critical yet understudied challenge due to misaligned feature spaces, disjointed modeling, and noise propagation in short-term interests. To address these limitations, we propose iFusion, a diffusion-based generative user interest fusion method, which reformulates interest fusion as a conditional generation process. iFusion leverages short-term interests as conditional guidance and progressively integrates long-term representations through denoising, eliminating reliance on linear fusion assumptions. Our framework introduces two key components: (1) the Disentangled Classifier-Free Diffusion Guidance (DCFG) Mechanism, which adaptively disentangles core preferences from transient fluctuations, and (2) the Mixture AutoRegressive Denoising Network (MARN), which enables joint interest modeling and fusion through autoregressive denoising. Experiments demonstrate that iFusion outperforms baselines across public and industrial datasets, as well as in online A/B tests, validating its effectiveness in robust CTR prediction. This work establishes a new paradigm for generative user interests  fusion in CTR prediction.

Published as a conference paper at ICLR 2026
IFUSION: INTEGRATING DYNAMIC INTEREST STREAMS VIA DIFFUSION MODEL FOR CLICK- RATE PREDICTION
Ziheng Ni Congeong Liu-xt, Yuying Chen', Zhiwei Fang', Changping Peng', Zhangang Lin', Ching Law*, Jingping Shao
1 JD.com 2 "Tee Hong Kong University of Science and Technology n z Lizihengestus stu. pku. edu. en, cl wew.hhhhooneeecc ust.hk
ABSTRACT
Click-through rate (CTR) prediction crucial for recommendation systems and online advertising, relying heavily effective user behavior modeling. While isting methods separately refine long-term and short-term interest representations, the fusion of these behaviors remains critical yet understudied challenge due misaligned feature spaces, disjointed modeling, and noise propagation in short-term interests. To address these limitations, we propose IFUSION, diffusion-basee generative user interest fusion method, which reformulates interest fusion as con- ditional generation process. IFUSION leverages short-term interests as conditional guidance and progressively integrates long-term representations through denoising, eliminating reliance on linear fusion assumptions. Our framework introduces two key components: (1) the Disentangled Classifier Free Diffusion Guidance (DCFG) Mechanism, which adaptively disentangles core preferences from transient fluctua- tions, and (2) the Mixture AutoRegressive Denoising Network (MARN), which enables joint interest modeling and fusion through autoregressive denoising. Exper- iments demonstrate that iFusion outperforms baselines across public and industrial datasets, as well as in online A/B tests, validating its effectiveness in robust CTR prediction. This work establishes new paradigm for generative user interests fusion in CTR prediction.
INTRODUCTION
Dynamic interest fusion stands as fundamental challenge in click-through rate (CTR) predictiorZhou et al. (2019): Chen et al. (2019), Zhou al. (018); Liu al. (2023a); Ni et al. (2025) Sang et al. (2025a b): Zhu et al. (2023) 2024) in recommendation systems and online advertising. At the core of this challenge lies the effective fusion of heterogeneous behavioral signals across different temporal scales particularly the integration of stable long-term preferences with volatile short-term interests.
The prevailing approach partitions user behaviors into long-term and short-term sequences (Chen let al. 2019). modeling them separately before fusion. Despite advances in individual components (Pi
al. 2020 Chang et al.) 20233 Si al., 2024 Hidasi et al., 2016 Xia et al., 2023), interest
fusion remains critically under-explored, particularly for handling non-stationary and contradictory
evolution patterns. Current fusion methods including concatenation (Zhou al., 2018), attention
mechanisms (Vaswani et al. 2017 Li et al. 2019), and gating networks (Hochreiter & Schmidhuber] 1997: Lv et al. 20199 rely on linear assumptions that prove inadequate, suffering from three key limitations: First, misaligned heterogeneous feature spaces arise from divergent feature selection mechanisms. (Shen et al.) [2022] Zheng et al.) 2022) Deployment constraints often enforce distinct representations for long- and short- terr interest modeling. For instance, long-term behaviors modeled using historical click logs (Pi et al. 2020: Si et al.] 2024), whereas short-term behaviors leverage purchase interactions (Zhou et al. 2018). This inherent heterogeneity creates non-linear interactions between temporal scales that conventional linear fusion operators fail to capture, as they
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Published as a conference paper at ICLR 2026
late fusion
W
perturbation
intttattiioonnnnsttttee interest entanglement
information isolation
information lose
interest
perturbation
promotion
long-term behaviors
short-term behaviors
misaligned features
Figure 1: Inherent limitations of current interest fusion methods
presuppose feature space alignment. Second, the limitations of disjointed modeling in late-fusson paradigms. Existing late-fusson methods (He et al. 2023) employ decoupled pipelines that isolate behavior modeling from interest fusion, resulting in suboptimal inductive biases that hinder effective cross-sequence integration. Third, perturbation-intere entanglement. Linear fusion lacks explicit mechanisms to disentangle meaningful interest signals from stochastic fluctuations in short-term behaviors, allowing noise to propagate unchecked and corrupt stable long-term representations. Moreover, HSTU-based generative solutions (Zhai al., 2024 Han et al. 2025) for ranking tasks intensify reliance on the richness of sequential information by constructing user historical behaviors into one unified fusion sequence. When behavioral data is sparse, this approach leads inadequate modeling performance due to its inherently discriminative nature in ranking tasks, which fails to effectively infer and fuse interests from limited behavioral evidence.
To address these challenges, we introduce iFusion, a novel framework that reformulates interest fusion conditional generation process rather than deterministic fusion operation. Our approach leverages diffusion models to gradually integrate long-term interest representations conditioned on short-term behavioral guidance through a structured denoising process. This generative formulation eliminates restrictive linear assumptions, enabling more flexible and robust interest modeling. The iFu- sion framework incorporates two synergistic components: the Disentangled Classifier- Free Guidance (DCFG) Mechanism, which explicitly separates core preference signals from transient fluctuations during the guidance process, and the Mixture AutoRegressive Denoising Network (MARN), which enables independent conditioning of multiple denoising pathways while capturing fine- anne-griiedd inter- est evolution. Together, these components provide a unified solution to the fundamental challenges of interest fusion. Our main contributions are summarized as follows:
Generative Reformulation of Interest Fusion: We propose a novel diffusion- frame- work for CTR prediction that treats interest fusion conditional generation process, moving beyond traditional deterministic fusion paradigms.
Leveraging the proposed MARN, we simultancously model the fusion of long- and short-term interests while capturing fine- fine-grained interest evolution within short-term behavioral sequences.
Joint Interest Modeling via MARN:
Disentangled Guidance with DCFG: By adopting the DCFG paradigm, we disentangle supervision signals explicitly quantify the contributions of core preference modeling and transient behavioral fluctuations.
Comprehensive Empirical Validation: We demonstrate the effectiveness of iFusion through extensive experiments public benchmarks, industrial datasets, and online A/B tests with CTR prediction serving as key evaluation scenario.
2 RELATED WORK
2.1
DISCRIMINATIVE USER BEHAVIOR MODELING
User behavior modeling plays a pivotal role in capturing user preferences, with widespread applica- tions in recommendation systems and online advertising (He al. 2023). Early approaches primarily
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relied MLP architectures (Wang al. 2015), later evolving to sequence-aware models such as RNNs (Hidasi et al., 2015; Hidasi & Karatzoglou 2018: / Li et al. 2017: Quadrana et al.
2017) and
attention mechanisms Vassani al. 2017] Zhou et al. 2018 8. 2019). Recent ad adnacees focus
modeling long-term dependencies (Ren et al. 2019 Pi et al. 2019. 2020
Chen et al.
2021| Cao
al. 2022), handling heterogeneous behavior types Xia et al. 2021
2020
Meng et al.
2020), and
integrating contextual side information (Li et al. 2020 Lei et al. 2021 1221iin
Liu et al. 202
Xie ct al.
2022).
2.2 GENERATIVE MODELING METHODS
Generative models have prompted a paradigm shift recommendation systems, transitioning from discriminative to generative approaches (Wei et al. 2025 Guo et al. 22025] Zhou et al.
2025a b Zhai
al. 2024 Han al. 2025). In particular, diffusion models have gained significant traction this domain (Yng et al. 2023 Li et al. 2023 Du et al. 2023 Niu et al. 2024 Wang et al. 2024 2023).
However, existing diffusion based work primarily focuses on sequential recommendation tasks, with limited exploration of generative paradigms for click-through rate prediction (Lai et al. (025). which requires effective modeling of dynamic user interest fusion, representing key limitation in the current literature.
PRELIMINARY
For each user, we consider two complementary behavioral sequences that capture different temporal granularities of user interests. The long-term behavior sequence B = { by ... ,,r represents the user's historical interaction patterns over an extended period, where M denotes the sequence length. The short-term behavior sequence is organized into sessions B = S2........ Sk J. where each session S, = [br, b.,, ..... .e,...,,,,,, bsS.) contains interactions within confined temporal window, and K' is the total number of sessions. We employ two specialized encoders to extract meaningful representations from these sequences. long-term behavior encoder f((.) that maps B dense representation h" Rd And short- terttterrm session encoder fs() that processes each session S, to generate session-level embeddings h RR Our objective is learn fusion function Fg that generates an enriched behavior representation:
h fusion
F (h))
where denotes learnable parameters. The fused representation h fusion should simultaneously preserve long-term user preferences and capture short-term dynamic interests for improved CTR prediction.
We combine this objective with diffusion model and reformulate the interest fusion process into Markov chain with T diffusion steps. In the forward process, the long-term interest h serves as the initial state Fp, and gradually adds Gaussian noise according a variance schedule {
T
(xx = (xx: / 1, ((xiT|X0))
Il xx-1)
(2)
t=1
We can directly sample x, at any time step / in closed form according to the notable property mentioned in (Ho et al. 2020):
(xx:\xo) (x Vānxo. (1-ā,)1)
where := 1- 8r and ā; := II a., The signal-to-noise ratio SNR(t) āz (1 āz)) decreases monotonically with ensuring XT converges standard Gaussian noise. The reverse process operates as denoising procedure that progressively refines the noisy long-term interest representation aaJossss T timesteps. Notably, we condition the denoising steps on the short-term interest signals (h 1 This ultimately generates an interest representation & that integrates long-term and short-term interests. The reverse Markov chain is implemented by neural network fo. formulated as:
P (b5)) = N 1 (2,, (b5) Eo ( (b))))))
For simplicity, we set शे XT
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((t (4-1))
X
Xy
long tirrttrmm interest (LT
forword process
Session Autoregressive Network
Z
Fg
R
MARN
ettlee Fexfons
reverse process
Finel Layer
hg
Prverss
interest (5S1)
pCTR
DCFG
(4) Overall framework of our proposed method iFusion
(b) Mixture Auto Regressive Network (MARN)
hp
acir
Scp
Str
Energy Layer
Concat
Concat
Iransient flow
Fusion Operator
C-Filter
T-Filter
MARN
MARN
share parmmosss
Spp
Stf
generative
Scasion Energ Filler
Disperse Energy Generate Flow
Energy Fusion
(c) DCFG guidance paradigm for interest fusion
Figure 2: The framework of our proposed method iFusion
where the mean function μı integrates short-term interest guidance while the variance Eg is typically fixed as ofI with of 3, learned. Following (Ho 2020), implement the mean prediction through noise reparameterization:
no W 4, (b (b.)
1
a4 /1 a. co(2,, (1))))))
(5)
Var
where Eg serves the guided noise predictor trained to estimate the injected noise step while being conditioned on short-term interest signals. We utilize an alternative reparameterization approach that predicts the generated interest representation & instead of estimating the additive noise as shown the equation 5
Va((1 ā(-1)
ng t, (b$1 = Vār-ifel&.tt (b5) +
(6)
VI
04
METHODOLOGY
Our proposed iFusion method effectively addresses the key challenges outlined in Section I As illustrated in Figure [ iFusion integrates multi-sequence user interests via generative approach, collaborating with discriminative modules to enhance downstream CTR prediction. It comprises two components: the DCFG mechanism, which provides robust guidance under perturbation for interest fusion, and the MARN, facilitating blended guidance throughout the inverse velocity field learning process.
4.1 DCFG: DISENTANGLED CLASSIFIER- GUIDANCE FOR INTEREST FUSION
CTR prediction face fundamental trade-off between signal fidelity and perturbation robustness when modeling user behavior sequences. While conventional classifier-fi guidance (CFG) (Ho & Salimans 2022) enables conditional generation, its uniform scaling approach proves suboptimal
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for behavior modeling where signals exhibit multi-scale characteristics from stable preferences to transient fluctuations.
Revisiting Classifier-Free Guidance. Given short-term session interest h as guidance g. standard CFG blends conditional and unconditional predictions with a single scaling factor ?:
fx t, = fo( +
t, t, (tl.tt)++offxxxx ((fo(x, fo(x,, t))
This formulation assumes homogeneous signal quality across behavioral contexts, an assumption frequently violated in practice. The guidance based on interest representation has a lower signal-to- noise ratio than the guidance used in traditional image generation tasks.
Energy-Baad -Based Perspective on Interest Dynamics. Drawing from stochastic thermodynam- ics (eeffent 2012), we model user interest dynamics as particles in composite potential field V(x (x199pp) + V(x, (ty). where core preferences Пep create deep attractors and transient fluctuations fef generate shallow perturbations:
dx [V
x (tp) + (V(xxl9)))) + V2DWW,
(8)
This analogy motivates our dual dual-component decomposition through functional separation. We imple- ment disentanglement through specialized architectures capturing complementary signal aspects:
hep ArgPool((ncooerr(g)) (low-pass filtering, C-Filter)
by Attenntio((Encoder(()) (high-pass filtering, T-Filter)
(10)
The core preference pathway employs strong regularization and global pooling for stability, while the transient pathway uses attention mechanisms to capture variations.
Generalized Energy-Based Formulation. We formulate conditional generation sampling from an energy-based based model with structurally distinct components:
p(xe(g)
( (x + Tie (x:lg))))
(11)
Theorem (Energy-Basel Disentanglement Guidance) Let E(x:19) Eep (x:lg) To (x (g) be the total energy function, where Eep and Eeg are implemented through archi- tecturally constrained networks with low-pass and high-pass characteristics respectively, Then the conditional score function admits the exact decomposition:
Xx log ((xlg))))) = - + +
(12)
Furthermore, if the architectural constraints enforce that the Hessians V2, Ecp and V2 E,g have approximately orthogonal dominant eigenspaces, then the guidance directions become functionally disentangled:
(~Vx, Vx Fas) S
(13)
where Ç quantifies residual correlation. (Proof Appendix K
Theorem I provides the theoretical foundation for DCFG, demonstrating that architectural constraints induce functional disentanglement without requiring strict conditional independence. Through Plakkr analysis of the Langevin system (Appendix D). we establish DCFG formulation derived based on Bayes' theorem in Appendix
folx,, (, = fx( + Σ
(xe,, - fx( ())
(14)
[E(cp.(f)
4.2 MARN: MIXTURE AUTO REGRESSIVE NETWORK
Current applications of diffusion models in recommendation predominantly rely on single-vector guid- ance during the reverse process (Yang et al. 2023), typically implemented through non-autoregressive (NAR) architectures such MLPs or Transformers. However, NAR methods might suffer from the inability to capture fine- ann-nnr sequential dependencies due to their parallel generation nature, leading to potential inconsistency suboptimal performance in tasks where require striet temporal
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coherence (Gu et al. 2017 Kasai et al. 2020) the guidance does not follow a completely linear relationship. Additionally, they may lack interpretability in modeling step-by-step decision processes compared to autoregressive alternatives (Stern et al. 2019). Inspired by the success of autoregressive (AR) modeling in sequential recommendation systems, we propose an autoregressive-based structure MARN for the reverse process. Unlike parallel injection methods that concatenate session embed- dings or use pooling methods, MARN processes K short-term session interests sequentially through chain-rule conditioning with the output for each session serving noisy representation for the next session's generation. Compared with the NAR method, MARN has better representation learning ability, more stable training and weight adaptation advantages, which are summarized in Theorem 2 detail proof provided in Appendix
Theorem (AR Superiority in Multi-Session Diffusion) Autoregressive (AR) injection strictly dominates non-autte non-autoregressvve (NAR) for multi-session diffusion with dependent sessions (3i, j [ss; 8j) 0), achieving: (1) tighter KL-bound DNAR D 5 - ((s
(2) O(K) lower gradient variance Var CAR) S Var(V Var(VLLAR)) and (3) adaptive weight- ing 0%k exp(-I[Vs, fll/oa)) NAR only competes when sessions independent under strict latency constraints.
This theoretically-grounded approach proves particularly effective for modeling multi-guidance user interest evolution, where the AR structure's ' ability to decompose complex joint distributions into conditional chains captures interest shifts more accurately than NAR's mean-field approximation. This advantage that grows superlinearly with session count (Figure 4b).
4.3 IMPROVING EFFICIENCY
To meet the low-latency requirements of online CTR serving, we optimize the diffusion- based interest fusion via consistency constraints. Traditional diffusion models suffer from slow inference due to iterative denoising steps, which is prohibitive for real-time applications. We introduce a consistency loss that enforces similarity between interest representations generated under different noise levels:
Lcons -t,pt=p((t) [l) t1) - fe (x(x (xxx)t)))11] t2)]
(15)
This constraint enables high-quality generation with drastically fewer sampling steps by learning noise-invariam representations. This makes diffusion models practical for industrial CTR systems while preserving their expressive power.
4.4 THEORETICAL ANALYSIS UNDER -ARA SCENARIOS
We analyze the zero-data scenario through the lens of denoising diffusion theory. The key insight is that diffusion models provide a principled mechanism for navigating the interest manifold M even with completely uninformative inputs.
Theorem (Diffusion Manifold Consistency) Under mild smoothness assumptions on the interest manifold M, the diffusion process generates representations that remain close M:
(=( S C [B[[] c0 1/21, 2x) + Cappmxx
(16)
Theorem 3 establishes that the learned score function guides the reverse diffusion process toward semantically meaningful regions of the representation space. This geometric constraint ensures robustness even when behavioral data is completely absent.
Theorem (Zero-Data Denoising Optimality) When 21; 2, are uninformative, the optimal denois- ing strategy converges to sampling from population-level statistics:
c0 t)
Ezg Pass [e]
(17)
This result demonstrates that our diffusion-based framework naturally falls back to reasonable population-leve priors in zero-data scenarios. Detailed derivations and experiment are provided in Appendix and N

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4.5 OVERALL TRAINING AND INFERENCE PROCESS
The overall training objective combines four loss components:
LE
Lgvol evolutionary
Dist disentanglerment
+
(18)
CTR prediction
consistency
true where LCF the cross-entropy loss, LFval = Φ Σi=1 Dens (s),++1 ((k++1 captures interest evolution,
Dis = g enforces guidance disentanglement, and Leons for improving efficiency. The training protocol of iFusion is shown Algorithm [ During inference, we employ an iterative denoising process starting from XT ht, At each step & from T to 1, we compute:
v 0 1a fo (x,, (h [ + Vor - (
Biz
(19)
1 Cr
This formulation represents single denoising step, which is applied iteratively until reaching the final fused representation Xo for CTR prediction. The inference protocol of iFusion is show in Algorithm
5 EXPERIMENTS
In this section, we present a comprehensive empirical evaluation of our proposed method. We mainly answer the following research questions: RQI: How does iFusion perform compared with other user behavior modeling methods? RQ2: How do difffernnt components of iFusion benefit its performance? RQ3: What the impact of factors (c.g., sample steps) on iFusion's performance? RQ4: How efficient is iFusion inference?
5.1 EXPERIMENTAL SETTINGS
Datasets and Evalution Metrics. We conducted experiments on benchmark including Amazon Book Datase Taobao Dataseland Ali Ads Datash are widely used user behavior modeling research, as well as real industrial dataset from a large internet advertising platform. A detailed description of the datasets can found in Appendix [ We use AUC as the evaluation metric for offline experiments. Furthermore, we adopt the relative improvement (Relalmpr) metric to evaluate performance difference between models (Zhou al., 2018 Yan et al. 2014).
AUC(model) 0.5
Relalmpr = AUC(base model) 0.5
x 100%
(20)
Baselines. We compare mainstream user behavior modeling algorithms including Avg-Pooling
DNN, DIN (Zoou et al.) 2018), DIEN (Zhou et al., 2019), SIM (Pi et al. 2020), ETA (Chen et al.
202T. SDIM (Cao et al. 2022). TWIN (Chang et al. (023). TWIN-V2 (Si et al. 2024). MTGR (Han et al. 2025), DiffuRec Li et al. 2023), DreamRec Yang et al. 2023) and DiffuMIN (Lai et al.
2025).
provided Appendix H.l All models were implemented using TensorFlow. For model training, we used Adam as the optimizer and trained each model for single epoch. More details can be found in Appendix H.2
Detailed descriptions of each baseline and implementation
5.2 COMPARISON WITH BASELINES (RQ1)
We evaluate iFusion against state-of-the-arr baselines in Table and provide the statistical signif- icance of our model's improvement over the best baseline model. Notably, in CTR prediction scenarios, 0.001 AUC gain is considered practically significant (Zhou et al. 2018 2019). Our experimental analysis reveals several key observations. First, both DIN and DIEN demonstrate superior performance over the standard DNN, validating the importance of attention mechanisms in modeling user behavior sequences. Furthermore, specialized architectures for long-term behavior modeling exhibit enhanced capability capturing extended user interest patterns. Methods like
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Table I: Performance comparison on four datasets. Best results in bold. All results run over times with std x le-3
Amazon
Taobao
Ali Ads
Industrial
Method
AUC
Relalmpr
AUC
Relalmpr
AUC
Relalmpr
AUC
Relalmpr
Traditional Methods
AvgPooling DNN
0.7689
0.00%
0.8539
0.00%
0.6352
0.00%
0.7512
0.00%
DIN
0.8162
+17.59%
0.8995
+12.88%
0,6422
+3.18%
0.7564
+2.07%
DIEN
0.8377
+25.69%
0.9222
+19.30%
0.6431
+5.84%
0.7611
+3,94%
SIM
0,8420
+27.18%
0.9268
+20.60%
0.6587
+17.38%
0.7625
+4.50%
ETA
0.8422
+27.26%
0.9272
+20.71%
0.6591
+17.68%
0.7625
+4.50%
SDIM
0.8426
+27.41%
0.9277
+20.85%
0.6596
+18.05%
0.7628
+4.62%
TWIN
0.8431
+27.59%
0.9288
+21.16%
0.6601
+18.42%
0.7630
+4.70%
TWIN-V2
0,8433
+27.67%
0.9289
+21.19%
0.6607
+18.86%
0.7634
+4.86%
MTGR
0.8440
+27.93%
0.9296
+21.39%
0.6615
+19.45%
0.7648
+5.41%
Diffusion-based Generative Methods
DiffuRecetr
0.8395
+26.26%
0.9258
+20.32%
0.6584
+17.16%
0.7607
+3.78%
DreamRecatr
0.8421
+27.22%
0.9286
+21.11%
0.6590
+17.60%
0.7619
+4,26%
DiffuMIN
0.8427
+27.45%
0.9288
+21.16%
0.6595
+17.97%
0.7623
+4.42%
Fusion (Ours)
0,8512
+30.61%
0,9347
+22.83%
0.6652
+22.19%
0.7685
+6.89
MTGR achieve additional gains through direct modeling of complete user behavior chains. Notably, existing biffusion-based approaches face limitations their guidance mechanisms, struggling to disentangle user preferences from transient fluctuations during interest representation learning. This entanglement issue hinders their performance in CTR prediction tasks. In contrast, iFusion addresses this fundamental challenge through interest decoupling guidance and autoregressive de- noising generation that progressively refines interest modeling. This principled approach yields statistically significant improvements over all competitive baselines, demonstrating the effectiveness of our proposed framework.
5.3 ABLATION STUDY (RQ2)
To systematically evaluate the contribution of each component in iFusion, we conduct comprehensive ablation studies. The results, summarized in Figure provide compelling evidence for our design choices. In Figure Sal our proposed DCFG demonstrates significant advantages over conventional CFG approaches. By separating guidance signals for core preferences and transient fluctuations, DCFG achieves more precise interest representation, reflected in the consistent AUC improvements. We observe that naively incorporating all guidance information leads to degraded performance in interest fusion quality. This finding underscores the necessity of our carefully designed guidance modulation strategy in interest fusion task, which selectively integrates relevant signals while fil- tering out noise. In Figure (3b] the introduction of MARN brings substantial gains, attributed to its hierarchical processing of interest guidance across temporal sessions. Increasing the complexity of MARN's internal networks does not yield significant improvements, rather than original sequence. It also suggest that the effectiveness stems from our interest-space fusion paradigm rather than network capacity. As shown in Figure 3c the consistency loss enables remarkable acceleration in inference generation while maintaining performance. This demonstrates the practical viability of iFusion for real-time industrial deployment.
are fusing interest space
5.4
STUDY (RQ3)
We conduct a systematic analysis iFusion's sensitivity to key hyper parameters: noise scheduling strategy, inference sampling steps, and the number of behavioral sessions. Our findings reveal important insights into the model's operational characteristics. Figure Aggure4allo (4a demonstrates that under consistency constraints, various noise schedules achieve peak performance with minimal sampling steps. The cosine schedule emerges as optimal, achieving best AUC with just single inference step. Notably, performance degrades with increased sampling steps, likely due to error accumulation in the iterative generation process. This observation validates our design choice for efficient few-step inference. As shown in Figure 4b the advantage of MARN's autoregressive processing becomes
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(17/9)
6.700
2 0.035
0.00
6.9
6.704
6.03
(a) Ablation on DCFG
(b) Ablation on MARN
(c) Ablation on consistency
Figure Ablation study on industrial dataset over runs (std le-3).
3.744
NAl
AR lettee
2.797
0.245
3.749
6.443
16
100
200
200
2
(nfkemrre
Sweatin Number
(a) Performance of different noise schedules differ- ent sample steps training with consistency loss
(b) Performance of AR (MARN) and NAR networks under different number of sessions
Figure 4: Hyperparameter study on industrial dataset over 3 runs (std z 1e-3)
increasingly pronounced with more sessions, confirming our theoretical analysis in Appendix t This scalability demonstrates iFusion's 's suitability for modeling complex interest fusion. Based on
our analysis, we adopt cosine noise scheduling with single-step inference the default configura- tion, achieving an optimal balance between performance and computational efficiency. Additional hyperparameter studies are provided in Appendix due space constraints.
5.5 EFFICIENCY ANALYSIS (RQ4)
We conduct a comprehensive efficiency evaluation of iFusion across both offline and online de- ployment scenarios. The results demonstrate the practical viability of our approach for industrial applications. In offline inference, by integrating model, we observe only a marginal 0.3% increase in inference time cost. This negligible overhead confirms the computational efficiency of our architectural design. In production environments, iFusion introduces merely a 0.302% increase TP99 latency compared to a highly-optimized industrial- grade model. Such minimal performance impact is well within acceptable thresholds for large-scale deployment. These efficiency metrics, coupled with the significant performance gains demonstrated in previous sections, establish iFusion prediction systems. Additional efficiency analyses, including training time convergence and memory utilization patterns, are provided in Appendix J space constraints.
interest fusion module atop the optimal bascline
effective and practical solution for real-world CTR
5.6 ONLINE A/B TESTS
We conducted large-scale online A/B tests over a 7-day period to evaluate iFusion's real-world performance in a production advertising system. The experiment involved hundreds of millions of users, with iFusion demonstrating statistically significant improvements across key business metrics: achieving a +2.44% gain in CTR (p_value < 0.001) and a +2.61% % gain in eCPM ip_value < 0.001). These results confirm iFusion's 's effectiveness in simultaneously enhancing engagement and platform monetization, while maintaining acceptable computational overhead as detailed in Section 5.5

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6 CONCLUSIONS
We propose iFusion, a diffusion-based generative framework for interest fusion that overcomes key limitations of existing methods: misaligned heterogeneous feature spaces, disjointed modeling in late-fusion paradigms, and perturbation-interes entanglement. By reformulating interest fusion as a conditional generation process, iFusion integrates long-term and short-term interest through decoupled denoising without restrictive linear assumptions. The framework incorporates two novel components: the MARN architecture, which jointly models interest evolution and fusion via autoregressive dynamics, and the DCFG mechanism, which disentangles core preferences from transient fluctuations to enhance robustness. Extensive evaluations in CTR prediction demonstrate the effectiveness of our approach. This work advances user interest fusion under diffusion paradigm, enabling fluctuation- aware interest modeling. The framework is readily transferable to other behavioral interest fusion tasks such CVR and GMV prediction.
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A THE USE OF LARGE LANGUAGE MODELS (LLMS)
In the preparation of this paper, we employed large language model primarily for language polishing and expression refinement. The LLM was used as assistive tool to enhance the clarity, coherence, and fluency the writing.
B NOTATIONS
Table Notation Summary
Symbol
Description
BL
long-term behavior sequence the i-th user's long-term behavior
M
number of long-tern user behaviors
gs
short-term behavior sequence short-term behavior sequence
S
the i-th user's s short-term behavior session
K
number of short-term user behaviors
long-term behavior encoder short-term session encoder
(s())
ht
long-term interest representation short-term session interest representation generated fused interest representation user interest fusion function diffusion steps
h
busion
F
T
B.
variance schedule for diffusion model the noisy representation time step
Xt
St
the noisy representation at time step f in the reverse process, for simplicity, we define žy Xy core preference guidance transient fluctuation guidance hyper-parameter to control the strength of core preference guid- ance and transient fluctuation guidance
9op
9ef
Njoī E (cp,tf}
C
DERIVATION OF EQUATION 14 VIA BAYESIAN GRADIENT DECOMPOSITION
Starting from the joint conditional distribution:
p(x: 9ops 9ts) = p(gep. 9ts xx)p(x:)
plyep: 9t5)
(21)
log p(e 9cps 9tt) = log p(gcpr 91f x,) + log p(x)) + const.
Taking the gradient with respect to Zr:
Vx, log p(x, Iep: gt.) = Vx, log logp(geps 9ts xx) + Vx, logp(x;)
(22)
Under conditional independence:
Vx, log pogep(ppp Sef xx) =
Σ
Vx, logp(g; x.)
(23)
iE (cppttff)
Reorganizing terms using the identity Vx, logp(gi xx) the final form:
Vx log D(x: gi)) Vx, logp(x;) yields
Vx, p(x Icps Its) = (1 - Yep Vs, logp(x;) + Yep Vx, log p(x: 9ep)
(24)
+ V. log p(ra (et))
Equivalent to:
Vx, log p(xp Hops Dts) Vx, log + Top (Vx, logp(x: gep) Vx, log (Xx))
(25)
+Vss(xx p(x ges) Vx. log (xx))
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D PROOF OF POTENTIAL DECOMPOSITION IN LANGEVIN DYNAMICS
D.1 PRELIMINARIES
Consider 2D overdamped Langevin system with position (z, v):
dr di
VV + (2>knTn(1))
(26)
where (t) (t), (t)) standard white noise satisfying:
(n:(t)) = 0
(t))) = 6tzō(t - -t)
Assumption (Potential Separability) The potential (ax, y) admits the decomposition: V(z, v) - Va( + Vv(x) + eVay (2,
(27)
where for the exactly separable case.
D.2 EXACT DECOMPOSITION THEOREM
Theorem (Dynamics Decoupling) For equation 27 the system equation 26 decouples
exactly into:
22 -z) + v 22kkBTnall (t()
(28)
rÿ (,y) + v TnkkBTTa
(29)
Proof The gradient operator acts on equation 27 as:
(,V2()) + +0, V (x, 0g (vv + ed,y Vzy (z,
VV
(30)
For = substituting into equation 26 yields immediate decoupling into equation 28 and
equation 29
The noise terms remain uncorrelated since: (n) (t))) = Vt.
(31)
D.3 PROBABILITY DENSITY FACTORIZATION
Lemma (FP Equation Decomposition)
The Fokker-Planck equation for equation 26 with
0:
OP = [ [(V))P
D KnT
(32)
0t
admity separable solutions P(x, y, 1) t,( (y,,t))
32 Proof Substitute the separable ansatz into equation
0
at (PaPy)
(V.V.
+
(a, PoP.P))
7 dr
dy
02
D
(P.Py)
oy (P.P,)
Or.2
Expanding derivatives and dividing through by PzPy:
0P. 1 ap, 1 (VV.P.)
0
1 82P
1 PP
+ P. 0t P, Øt 7 Ps dz
(0, (0
+D
+
Py dy
Pe dx
Py dy²
(33)
This separates into two independent equations:
OP, 1 a
Ot dz (V.V.Ps) D P
(34)
dr²
aP, 1 0
P.P
d6 7 Dy (0, V P,) + 1)
(35)
confirming the solution's 's separability.
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D.4 PERTURBATION ANALYSIS FOR WEAK COUPLING
For e <,, we can expand the solution as:
P(a.y.t) P! (z, (v. + (z.t,)+)
(36)
where the supenscript (0) denotes the separable solution. The first-order correction satisfies:
ap Lip + Lyp
(P!o) p VV
(37)
at
with Li being the FP operators for each coordinate. This shows how non-separable terms introduce coupling between directions.
E THEORETICAL ANALYSIS OF AR VS. NAR IN MULTI-GUIDANCE INJECTION IN DIFFUSION REVERSE PROCESS
The core challenge lies in effectively integrating K short-term behavior sessions $1.K into the diffusion reverse process. We analyze two fundamental approaches through three lenses: Modeling Perspective. When sessions exhibit weak dependence (I(s;; S <), the autoregressive (AR) injection's 's chain rule decomposition achieves strictly lower approximation error. For any diffusion step t:
Theorem (Approximation Error Bound) The KL-divergence gap between AR and non- autoregressive (NAR) injection satisfies:
K
NAR KL
DAR KL
1
Σ ((g88)
(38)
2
K
k=1
Weight mismaach
Correlation penalty
Proof 3 The AR model's 's sequential processing preserves conditional dependencies through exact probability chain rule:
K II
PAR X (60xx-111111 (1:k)
p(x--118k X,, 81:k-1) 1=1
(39)
whereas NAR's concatenation forces mean-field approximation, introducing the weight mismatch term. The correlation penalty emerges from Jensen's inequality applied the joint distribution.
Optimization Dynamics. The gradient behavior differs markedly due to injection architecture. AR's sequential nature induces implicit gradient averaging:
Theorem (Gradient Variance Ratio) For L-Lipschitz denoising networks with session weights ag!
Car(VoLNAR)
K
J 12
(40)
Varl V (ARR)
L
Σ
Ek=1
a
where J denotes the input Jucobian matrix.
Proof 4 AR's variance scales as O(1/K) due to Central Limit Theorem effects in sequential pro- cessing, while NAR's variance grows with input dimension dmn
VarNAR & E [o,.a sk]ll K din
(41)
The ratio follows from direct variance computation and Lipschitz continuity.
Adaptive Weighting. AR models automatically lear noise-sensitive session weights without explicit design:
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Proposition 1 (Implicit Attention) At noise level dt AR's effective weights satisfy:
,, 0x
(t) exp
(42)
Through backpropagation, the gradient norms IV Gii act as implicit importance scores. Higher noise levels o, soften the weight distribution, matching the intuition that session relevance becomes umbiguous in early diffusion steps.
Proof 5
Table 3: Practical Implications Summary
Property
Interpretation
Lower KL-bound
AR better preserves complex session interactions
Gradient stability
AR's s SNR improves with more sessions (xx vK)
Dynamic weighting
AR adapts session relevance without architecture changes
The theoretical findings suggest AR injection is preferable when: (1) session correlations exist
(e > 0), (2) model capacity permits sequential processing, and (3) gradient stability is critical. NAR remains competitive when sessions are truly independent or computational latency dominates quality concerns.
PSEUDOCODE FOR IFUSION TRAINING AND INFERENCE
Algorithm iFusion Training Protocol
Require:
Training set DD core signal extractor I1, fluctuation extractor 92
Require: Dropout rates Pareg Parop Initialize all model parameters.
1: repeat
2: (h+, k1) D
Get long-term and short-term behavior interests.
3: No hL
4:
f1,t2 ~ u(f1,.....
Sample diffusion step. Sample Gaussian noise.
5:
E1,62 ~ 1)
6:
for foreach { E E1), (12, e2)) do following step:
7:
X: VapXo + v1 are
> Forward process. > Disentangle guidance signals
8:
I:K 1:K = = e2 (h1:k)
9:
1 Parop)
Perform unconditional training with probability Pämp
10:
for mode in {cvo, task) do
11:
If mode = evo then idæ = 1 K clse idæ = 2 +
D Select used sessions
12:
Pep gmode m1 gizz + (1 m1)
Dts m2 lef + (1 m2) 0
mmode
14:
fo fmode 7ou fo (x,, t, t...t,9pamn gmodee)
76 Tep t
fo (xx, t, gmodee)
equation
Top Top
end for
16:
L= LCF \₁ LEvol + X2LDia + AzCcons
equation B8
17:
Update x- VoL
18:
until convergence
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Algorithm 2 iFusion Inference Protocol
Require: Trained model fo, trained core signal extractor 91, trained fluctuation extractor
Require: long-term interest h+,, short-term sessions hf.K
Ensure: generated interest Xo
1: Initialize XT with long-term interest h
2: 9ep - e1 ()) e2(h).))
3: for t = down to 1 do
4:
~ N0 1) if > else z =
5:
Funcond (x,, L,
6:
fep = folX,, t, (g l:K)
7:
für = fo(x,,t, t, (gif
1:K
8:
f = funcond + EjE(epAR) Ts - funcond)
> Theorem 14
9:
X1-1
v f (an(1-ār-1) l-as
1-
Xe + B/zz
10:
end for
G DETAILS OF DATASET
To better demonstrate our method's capability in addressing the challenges presented in this paper, we have made several deliberate and distinctive choices in constructing the dataset, particularly regarding the public dataset. Below, we provide a detailed overview of the dataset.
Amazon Dataset (McAuley al. 20155) is commonly used benchmark in user behavior model- ing (Zhou et al.) 22118) 2019). It comprises product reviews and metadata from Amazon, specifically utilizing the Books subset, which includes 75,053 users, 358,367 items, and 1,583 categories. this dataset, reviews are treated as interaction behaviors and are chronologically sorted per user, with maximum behavior sequence length of 100. Following common practices in related work, we split the sequence into short-term and long- tng-term sequential features, using the most recent 10 interactions for short-term modeling and the preceding for long-term representation.
Taobao Dataset contains approximately one million randomly sampled users, recording all their interactions (e.g., clicks, purchases, add-to-cart, and likes) between November 25 and December 2017. The dataset includes user IDs, item IDs, item category IDs, behavior types, and timestamps. Following (Cen et al. 2021). }, we first sorted each user's behaviors chronologically. Unlike previous works that rely solely click data, intentionally incorporate all behavior types when constructing the short-term behavior sequence. This design choice allows us to rigorously evaluate our method's capability in addressing the first challenge: misaligned heterogeneous feature spaces arising from divergent feature selection mechanisms, as different behavior types naturally introduce variations in feature distributions and semanties. For the short-term behavior sequence, we extracted the most recent 16 behaviors. Mcanwhile, the long-term behavior sequence was constructed directly from the user's most recent 256 behaviors.
Alibaba Ads Dataset is provided by Alibaba, which is display advertising click-through rate prediction dataset. It includes shopping behavior data from all users over a 22-day period and includes comprehensive information on users, advertisements, and user behaviors.
Industrial Dataset is the traffic logs from the advertising system of a large internet platform. 10 billion click- click-through logs in the first 32 days are used for training, and 0.5 million from the following day for testing. We define the user's click behavior from the past 180 days as the long- term behavior sequence, while the interactions from the most recent 60 days form the short-term behavior sequence. Notably, this short-term sequence represents a heterogeneous mixture of search queries, item clicks, and purchase actions, which directly exemplifies the challenge of misaligned heterogeneous feature spaces arising from divergent feature selection mechanisms as outlined in our introduction. Furthermore, the short-term sequence is segmented into sessions based predefined rules to better capture temporal patterns and user intent transitions within these behaviorally diverse interactions.
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H DETAILS OF EXPERIMENT SETTINGS
H.1 DETAILS OF BASELINES AND IMPLEMENTATION
We compare mainstream user behavior modeling algorithms including:
Avg-Pooling DNN: All user behaviors are treated equally with the sum pooling operation.
DIN: DIN introduces attention mechanism to capture user interests by adaptively weight- ing the historical behaviors based on their relevance to the target item.
DIEN: DIEN models the dynamic evolution of user interests over time using a GRU-based network with an auxiliary loss to enhance behavior representation learning.
SIM: SIM extracts user interests with two cascaded search units: (i) General Search Unit (GSU) acts as general search from the raw and arbitrary long sequential behavior data, with query information from candidate item, and gets a Sub user Behavior Sequence (SBS) which is relevant candidate item; (ii) Exact Search Unit (ESU) models the precise relationship between candidate item and SBS.
ETA: Compared with SIM, the most important improvement of ETA is the change of the retrieval method in the GSU stage, which changes the simple eategory-based retrieval of SIM to the retrieval based on Hamming distance after the item embedding is processed by SimHash.
SDIM: SDIM sums the embeddings of items with the same hash as the target item in the user behavior sequence and normalizes them to obtain the user interest expression, further reducing the time complexity to O (Imlog (d)) B represents the number of candidate sets, L represents the sequence length, d represents the item embedding dimension, K represents the top K items selected, and m represents the number of different hash functions used in SDIM.
TWIN: TWIN is a two-stage interest network for lifelong user behavior modeling, in which Consistency-Preseee Presservd GSU (CP-GSU) adopts the same target behavior correlation measure as TA in ESU, making the two stages twins.
TWIN V2: TWIN-V2 compresses lifecycle behaviors through clustering and discovers more accurate and diverse user interests. In the offline phase, hierarchical clustering method groups items with similar characteristics in lifecycle behaviors into clusters. By limiting the size of clusters, behavior sequences can be compressed facilitate online reasoning in GSU retrieval. Cluster-aware target attention extracts users' long-term interests, resulting in more accurate and diverse recommendations.
MTGR:
MTGR integrates the advanced DLRM and GRM modes, retaining the characteris- tics of DLRM such as the Cross feature, while verifying the excellent performance of GRM and proposing Group-Layer Norm and dynamic masking strategies.
sequential recommendation model that constructs dynamic item representations through controlled noise injection, enhancing uncertainty modeling in user preference learning.
DiffuRec: DiffuRec is the first diffusion
DreamRec:
DreamRec is diffusion-basse sequential recommendation method that learns to generate target items by denoising with guidance from historical interactions using classifier-fi guidance mechanism. climinating the need for negative sampling while directly capturing user preferences. In our implementation, we use long-term behavior as the starting point of the forward process and use the fusion representation of short-term behaviors from multiple sessions guidance.
DIffuMINN: DiffuMIN is designed to model long-term user behavior and deeply explore the user interest space. uses a goal-oriented multi-interes extraction method that first performs an orthogonal decomposition of the goal to obtain interest channels. It then decouples and extracts multiple user interests by modeling the relationship between interest channels and user behavior. A diffusion module guided by contextual interests and interest channels is then employed. This module anchors the user's personalized and goal goal-oriented interest types, generating enhanced interests that consistent with the user's 's latent interest space, further exploring the restricted interest space. Finally, contrastive learning is utilized to ensure that the generated enhanced interests are consistent with the user's true preferences.
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H.2 MORE DETAILS OF IFUSION IMPLEMENTATION
Our long-term encoder employs a double-layer transformer encoder-decoder, while the short-term en- coder uses single-layer transformer encoder to process each session independently. The transformer configuration includes hidden size of 64 and 4 attention heads. We use the Adam optimizer with learning rate of 0.001 and eps set le-7, without gradient clipping. For Amazon dataset, short-term sequences consist of the most recent 10 behaviors and long-term sequences the most recent 90, For Taobao dataset, short-term sequences include 16 behaviors and long-term sequences 256. The batch size is 512 on the industrial dataset, and 256 on both Taobao, Amazon and Ali Ads.
Figure 5 shows the online deployment our generative interest fusion method, iFusion, in CTR prediction for real-world industrial advertising system. iFusion constructs training data based on historical backflow logs to update the model. During online service, iFusion accepts inputs including user static features, ad features, user behavior sequence features, and candidate ad information. It outputs an estimated PCTR value, which is used for eCPM calculations in the downstream advertising system.
Ad Ad
Users benogrophy Datebese
Real- Real-time
Prediction
Ad Features
Users Behavior Featunes
Computation
Model
Realtime behovior event
leamnr
Finol Layer
PCTR
concat
other
iFusion
revense guidance
forward
start
Energy Loyer
lang- Ferm interest:
short session interest
short-torm interest
Long becoder
Session Encoder
Short Decoder
Embedding Layer
long-term behaviors
short-term behaviors
Figure 5: Real-time online deployment diagram of iFusion
I ADDITIONAL HYPER- STPER-PARAMETER ANALYSIS FOR IFUSION
I.1 THE IMPACT OF DIFFERENT GUIDANCE STRENGTH
We conducted hyperparameter experiments on the DCFG guidance strength hyperparameter with candidate ranges for both "cp and " ranging from [0, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0, 3.0, 4.0. 5.0], for a total of 25 combinations. Our results show that method achieves the best results (AUC=0.7685) when Yep is set to 2.0 and fh is set to 1.0, leading us to conduct additional comparison and ablation experiments based this. We also found that when Tep is 1, meaning that core interest
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guidance is included, method performs the worst among all settings, and this performance increases with increasing "ey. Setting the "Yep to "e parameter ratio around 2.0 achieves relatively optimal results.
J EFFICIENCY ANALYSIS OF IFUSION
We analyze the efficiency of our proposed method iFusion from both offline training and online inference perspectives. Compared our online base model, our diffusion-basee approach incurs modest 0.97% increase in training time on A100 GPU due to its iterative denoising process. For online serving, the step-wise generation introduces only 0.302% latency increase while maintain- ing real-time responsiveness. These computational overheads are well justified by the significant improvements in recommendation quality, with significant AUC gain offline and CTR improvement online. The marginal efficiency costs are practically negligible in production environments, making our method highly feasible for real-world deployment. Training time and GPU memory utilization shown in Table 4
Table 4: More Efficiency Information
base model
iFusion(ours)
Train Memory (GB) Training GPU utilization
63.6
63.7
79.50%
79.63%
Inference Memory (GB) 9.5
9.7
Inference GPU utilization
11.87%
12.12%
K PROOF OF THEOREM 1: ENERGY-BASED DISENTANGLEMENT
In this appendix, we provide detailed proof of Theorem 1 from Section which establishes the theoretical foundation for our Disentangled Classifier-Free Guidance (DCFG) framework.
K.1 PRELIMINARIES
Recall that we model the conditional generation process using energy-based formulation:
(g) exp( exp((E(xx1g)) exp( Ig) + +
(xeeg))
(43)
where Eep and E,g are energy functions implemented through architecturally constrained networks. The score function is defined as the gradient of the log-probability:
V log p(x Vx. E(x,
(44)
K.2 PROOF OF SCORE DECOMPOSITION
From the definition total energy function:
(' Tep Exp + Tes Exx((l))))
(45)
Vx. x, Fx Nep Vx, Eep (x+10) + xx, Fas (xxl9)
(46)
Substituting into the score function definition:
Vx, logp(x: ig)
-V xx E(xelg)
(Top Vx, Eep (X1 9)) + Me Ex, Exr((x(g))
(47)
7 Frop (x(lg) + Toy( (- Vx, F (x,lg)
This completes the first part the theorem, showing the exact decomposition of the conditional score function.
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K.3 PROOF OF FUNCTIONAL DISENTANGLEMENT
We now prove that under appropriate architectural constraints, the guidance directions become functionally disentangled.
Let Vep = Vx Pep and V.s - xx, Fes represent the guidance directions for core preferences and transient fluctuations, respectively. The inner product between these directions is:
(Veps Vtf)
Vif
(48)
We analyze the conditions under which this inner product is bounded by small value Ç. Consider the Taylor expansion the energy functions around a point xP:
Eep(Xe) = (xp((x)) + V Eeep(x)) (x)) x()
2 [x, x H Hep(x; xx.)
(49)
1 2(xx x H H x.)
(x)(xi) R (x) + (x,
(x+ xx) (x))xxxxxx++>>)>)))))))))))))(
(50)
where Hep and Hyf are the Hessian matrices of Eep and Esg, respectively. The architectural constraints imposed on our networks ensure that:
1. Eep has low-pass characteristics, meaning Hep has dominant eigenvalues corresponding to slow-varying directions the input space.
2. Fsy has high-pass characteristics, meaning H, has dominant eigenvalues corresponding to high-frequency directions in the input space.
Let uu tively. Similarly, let (uf u2 .....,..... uf/ and (Xi, , > be the eigenvectors and eigenvalues of H.y, respectively.
..... .,...., " and {ATP, A2 ..... ,....., X be the eigenvectors and eigenvalues of Hep, respec-
The architectural constraints ensure that the dominant eigenspaces of Hep and H,, approximately orthogonal:
(u u ~ 0 for < k.j 1 where & and are the indices of the dominant eigenvalues for each Hessian.
(51)
Now, consider the gradients:
E ~ Hxp(x: - x() VES R Hxy(x, - x!)
(52)
(53)
The inner product of the guidance directions is: (Veps Vaf) (- 1-VE Eop, -VEr)
(54)
(xt - x His((x x()
Expanding (x: xxf) in the eigenbases:
x, x
a Σ byu
(55)
Then:
(Vep: Viy) 2
Σ auuu"
H
Σonuzs
(56)
asbs(u cP) H Horuy
Since H.p and Hef are symmetric (for diffeeiifferentiable energy functions), we have:
(
H cp (u
(57)
H Hy 15 = u//
(58)
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Thus:
10 78
(Veps Vif)
a 1 (u
(59)
Due to the approximate orthogonality of the dominant cigenspaces, the terms (u,") ff are small for the indices i.j where X," and X are large. The remaining terms involve at least one small eigenvalue, making the product X x small.
Therefore, there exists bound ( such that:
(Veps Vog) <
(60)
This proof demonstrates that through appropriate architectural constraints that shape the Hessian matrices of the energy functions, we can ensure that the guidance directions become approximately orthogonal. This functional disentanglement occurs without requiring strict conditional independence between the behavioral components, making our approach more robust and practical for real-world CTR prediction tasks. The value of Ç depends on the degree of orthogonality between the dominant eigenspaces of H. and H, which in turn is controlled by the architectural choices (e.g., pooling vs. attention) and regularization strategies employed in our framework.
L MORE DETAILS OF DCFG IMPLEMENTATION
Table 5: Key components of the DCFG framework implementation.
Component
Implementation
Core Preference Encoder
Average- + MLP (strong regularization)
Transient Fluctuation Encoder
Attention + MLP (weak regularization)
Orthogonality Constraint
Gradient cosine similarity minimization
M PROOFS OF ZERO-DATA THEOREM
M. PROOF OF THEOREM
We consider the diffusion process defined by the forward SDE de f(z, t) g())dw and the reverse SDE dz = [f( ())g(())V, log (=(=)ddt+g((tdd The score function V, (z))) captures the local geometry of the data distribution. On the manifold M. the score function points toward regions of high data density, effectively guiding the reverse process along the manifold. By the manifold hypothesis, the data distribution Pdata concentrated on M, so the learned score function
satisfies:
V log.)())) Projr, (V. (x))
(61)
where Projr; is the projection onto the tangent space.
The denoising process aims to recover from noisy observation can be bounded:
The expected denoising error
El
20 < C V (z)) fo(2,t)11
(62)
< C2 E[[l+ ea ( 1)] + Eappoox
where fo is the learned score function. Since the true data lies on M, and the denoising process is consistent:
d=<(zo 2²) S 1120 = s L (1)=0 &oll + +1 - =²
(63)
In zero-data scenarios, the conditional information 24, 2. is uninformative. The diffusion process falls back to the unconditional score function V log (2), which still guides the generation toward the interest manifold M.
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M.2 PROOF OF THEOREM
In the unconditional setting (zero behavioral data), the optimal denoising function minimizes:
(()) = Ex..,, |||- cs(z). 1)
(64)
The solution this optimization problem is given by: (2, t) B[c|2]] Po [c|2]]
(65)
This follows from Tweedie's formula and the Gaussian perturbation structure of the forward process:
= V 20 + /1 OzE
(66)
Thus, in zero-data conditions, the denoising network learns to predict the expected noise given the noisy imput, effectivelly performing Bayesian estimation using population-level statistics. The conditional case with uninformative 21, 2g reduces to the unconditional setting.
N EXPERIMENT ON THE EFFECT OF IFUSION IN ZERO-DATA SCENARIOS
We evaluate iFusion's robustness under two dimensions of user behavior sparsity: (1) absence of historical long-term behavior, and (2) scarcity of recent interaction records.
For the first scenario, we simulate missing historical data by deliberately omitting long-term interest representations during evaluation. Comparative analysis shows that iFusion's generative interest fusion module effectively compensates for the lack of long-term context, achieving an AUC gain of approximately 0.003 compared to traditional discriminative methods. This demonstrates the model's ability to infer stable interest representations despite incomplete historical data.
For the second scenario, we systematically reduce the number of available sessions and pad the corresponding interest representations. Traditional discriminative fusion methods exhibit significant performance drops due to their reliance on complete recent interaction sequences. In contrast, iFusion's generative paradigm substantially mitigates this sensitivity through its sampling- compling-based based generation process, which derives plausible interest points by leveraging the learned fused interest. distribution and available long- interest patterns. This approach maintains robust performance even with limited recent behavioral data.
O FEATURE MISALIGNMENT IN LONG-TERM AND SHORT-TEM INTEREST MODELING
The challenge of feature misalignment in long and short-term interest modeling is acknowledged in related work (Shen et al.] 2022] Zheng et al., 2022). Prior work (Shen et al.) 2022 Zheng al. (022) explicitly discusses the challenges of integrating heterogeneous user behaviors, where different interaction types like clicks and purchases carry distinct semantic meanings and naturally create feature space discrepancies between long-term and short-term sequences. Additionally, this misalignment stems from fundamental constraints in industrial deployment. In practice, long-ten term sequences typically contain only click behaviors due latency constraints in online serving, while short-term sequences incorporate diverse behaviors (clicks, purchases, cart additions) to capture recent dynamic interests. This engineering reality creates an inherent heterogeneity between long- term and short-term feature spaces. Furthermore, users exhibit varying dependencies on long-term versus short-term interests across different contexts, making effective fusion even more challenging.
P THEOREM I's ORTHOGONALITT
The orthogonality of core signals and transient fluctuations within a session primarily impacts DCFG module without propagating system-we wide constraints. Several works. such End4rec (Han et al.) 2024). SdifRec (Xie et al. (2244 and FMLP-Rec (Zhou et al. 2022), have proposed similar assump- tions regarding the orthogonality of core signals and transient fluctuations. The disentanglement loss
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DiS ( (equation 18] explicitly minimizes the inner product between the core and fluctuation guidance vectors during training, which is a direct implementation of the orthogonality constraint. Its positive contribution in ablation study implicitly validates this design. And we conduct additional experiment to directly measure the cosine similarity between the guidance gradients - -V Eep and Eay- The results held-out validation set show consistently low average cosine similarity of less than 0.102, providing concrete empirical evidence for the "approximate orthogonality" asserted in Theorem 1. We monitor this similarity throughout the training process. The value rapidly decreased and stabilized around the low level after few batch, demonstrating that the disentanglement is not transient but a stable outcome of our training objective and architectural design.
Q SESSION MUTUAL INFORMATION IN THEOREM 2
We estimate the session mutual information (5) our datasets. Given that short-term sessions are segmented from recent continuous behaviors, they naturally reflect similar contextual factors (e.g., promotional influences, purchasing power), leading positive mutual information. However, due to the diverse intents and preference fluctuations across sessions, the mutual information values estimated to be moderate low level. This precisely aligns with the dependency condition in Theorem 2 (115 85) > 0), where AR injection provides significant benefits over NAR methods, while the moderate-low value explains why the advantage grows superlinearly rather than linearly.
R EXPERIMENTS ON TRAINING EPOCHS
Single epoch is the mainstream setup in research related to CTR prediction (Zhang et al., 2022
Cao et al. 2022 Liu et al. 2023b Yan al. 2024). As documented previous studies Zhang
et al. 2022 Cao al. 2022 Liu al. 2023b Yan et al. 2024), CTR models often exhibit one- epoch phenomenon where models begin to overfit after the first epoch, with test set performance degrading sharply upon entering subsequent training cycles. To rigorously validate this pattern in experimental setup, we conduct comprehensive multi-epoch verification across all compared methods. The results consistently show that while all models achieved their performance peak within the first epoch, each method exhibit varying degrees of performance degradation when training continued through additional epochs. This empirical evidence confirms that single-epoch training not only aligns with CTR prediction task standards but also ensures we compare all methods at their optimal performance point. We have included the complete multi-epoch analysis in the appendix in our revised version.
Table Performance comparison across different epochs
Method
Epoch
Epoch 2
Epoch
AvgPooling
0.7512
0.7491
0.7457
DIN
0.7564
0.7548
0.7507
DIEN
0.7611
0.7589
0.7550
SIM
0.7625
0.7601
0.7574
ETA
0.7625
0.7603
0.7575
SDIM
0.7628
0.7604
0.7576
TWIN
0.7630
0.7607
0.7580
TWIN-V2
0.7634
0.7609
0.7581
MTGR
0.7648
0.7619
0.7687
DiffuRec
0.7607
0,7582
0.7544
Ree
0.7619
0.7595
0.7558
DiffuMIN
0.7623
0,7596
0.7560
iFusion
0.7685
0.7652
0.7618
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S SENSITIVITY TO DIFFUSION STEPS T
The training I' is set to 100 for all experiments. Due to the consistency distillation, the performance of our one-step inference model is remarkably robust the original choice T' As shown in Table varying training 7' has minimal impact on the final one-step inference performance.
Table 7: Performance comparison across different training steps T and datasets
Training T AUC (Industrial)
AUC (Amazon)
AUC (Taobao)
AUC (Ali Ads)
50
0.7683
0.8508
0.9344
0.6651
100
0.7685
0,8512
0.9347
0.6652
200
0.7684
0.8510
0.9343
0.6649
T GENERALIZABILLTTY ACROSS MODEL ARCHITECTURES
Using a simple DNN tower as the final prediction layer is standard and controlled practice this line of research (e.g., in DIN, DIEN, SIM) because it prevents the effects complex prediction tower from confounding the evaluation of the core behavior modeling module. iFusion framework is modular and agnostic the final prediction tower. Its output, the fused interest representation h hiusioon is unified vector that can serve as input to any downstream CTR model. To demonstrate broader applicability of iFusion, we conduct experiments integrating the -Fusion-generatte representations with DeepFM, DCN and Autolnt backbones. The results consistently show that iFusion provides significant and stable performance lift over the baseline behavior fusion methods when used with these advanced architectures.
Table 8: Generalizability of iFusion Across Model Architectures
Backbone
Method
AUC
Rellmpr
DeepFM
MTGR
0.7652
0.00%
DeepFM
iuusion
0.7690
+1.43%
DCN
MTGR
0.7658
0.00%
DCN
iFusion
0.7697
+1.47%
Autolnt
MTGR
0.7663
0.00%
AutoInt
iFusion
0.7704
+1.54%
Table Experiment of LTI and SSI Encoder
LLI/SSI
iFusion
AUC
Att-hased
w/ 0.7685
Aut-based
w/o 0.7641
w/ AvgPooling
w/ 0.7639
w/ AvgPooling
w/o
0.7602
U DETAILS ABOUT LTI AND SSI
In our implementation, both the long-term and short-term encoders are based on the widely adopted attention-based architectures commonly used for user behavior sequence modeling in CTR prediction. This choice ensures fair and practical comparison with existing state-of-the-an methods. The iFusion framework, is designed to be agnostic to the underlying encoder architectures. The primary role of these encoders is to provide a competent initial representation of user interests. We conduct additional ablation studies where varied the base architectures of the LTI and SSI encoders. The results consistently show that while the absolute performance baseline shifts with different encoders, the iFusion module consistently provides stable and significant performance boost across these
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variations. This demonstrates that the performance gains are primarily attributable to our novel fusion paradigm rather than to a specific encoder design. Therefore, while the encoders are necessary components process raw behavior sequences, the specific choice of their architecture is not the critical factor for iFusion's 's superior performance.
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Reviews:
[{"review_id": "Vz3TjqkIAn", "reviewer": "Reviewer_yR4B", "summary": "This paper aims to tackle interest fusion for CTR predictions. Given a long-term sequence and a short-term sequence partitioned into sessions, the goal is to learn a fusion map for downstream CTR. The key insight is to treat fusion as conditional generation with a diffusion model conditioned on the short-term sequence embeddings. The authors argue that such a generative fusion paradigm that disentangles stable vs. transient short-term behaviors during guidance and autoregressively conditions on multi-session contexts can yield consistent offline gains and online lieft with negligible latency. Specifically, this paper employs two mechanisms to structure the conditional guidance and how sessions are injected, namely DCFG splits guidance into core‑preference (low‑pass C‑Filter) and transient fluctuation (high‑pass T‑Filter) components with tunable strengths $\\gamma_{\\rm cp}, \\gamma_{\\rm tf}$, yielding an additive score decomposition supported by an energy‑based view and architectural assumptions about Hessian eigenspaces; MARN injects the $K$ shot-term sessions autoregressively in the reverse process via chain rule conditioning. For low latency, a consistency loss is used to encourage step‑invariance, which helps to enable one‑step inference. The proposed method demonstrate AUC improvements by 0.003-0.007 over the strongest baseline on multiple datasets; an online A/B tests reports improved CTR and eCPM with increased latency. The ablation study on the industrial set demonstrates the usefulness of the incorporated components.", "questions": "1. Why limit all baselines to one epoch? It is suggested to report learning curves or early-stopping results showing that additional epoch would not change the performance.\n2. In Figure 3b, AR-Attn slightly exceeds Ours. Which added components reduce AUC and why are they kept in Ours eventually?\n3. It is also suggested to report gradient/Hessian alignment between C-filter and T-filter over training to substantiate functional orthogonality claims.\n4. Can you please precisely define the NAR family and constants in Theorem 2 and estimate session mutual information on your datasets to check the stated dependence condition?\n5. What is the training $T$ and how sensitive are results to $T$ when using one-step inference?\n6. Beyond Appendix N, it would be interesting to show the method's robustness under somehow behavioral shocks (e.g., the promotion windows as shown in Figure 1), cold-start sessions, and heterogeneous event mixtures.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed method is intuitive, based on the common understanding of user behaviors, and reasonably demonstrated.\n2. The DCFG formalizes the multi-scale guidance, and the MARN motivates AR conditioning for the dynamics across multi-session contexts. \n3. The breadth of empirical studies is commendable, which includes four datasets (with the industrial dataset included as well).", "weaknesses": "1. The core motivation targeting the claimed research gap. That is, the long- and short-term features are misaligned, while being intuitive,  was not empirically demonstrated, nor has solid support from literature.\n2. Theorem 1's orthogonality is assumed via architectural constraints, namely C-filter and T-filter can induce orthogonal dominant eigenspaces in the Hessians of their respective energy functions. This is a strong assumption that was not empirically verified.\n3. The bound presented in Theorem 2 omits definitions and conditions.\n4. All models were trained for one epoch without justification. In practice, many baselines typically benefit from longer training, nor the authors provide learning-curve evidence, which raises concerns regarding the small AUC margins as the claimed performance gain could shift given reasonable hyperparameter tuning. Eventually, this affects the strength of the offline comparison.\n5. No anonymous code or explicit reproducibility statement. While some details are partly included in the appendix but I personally don't think they are sufficient for full replication.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "D3fBlJVyMy", "reviewer": "Reviewer_n48p", "summary": "This paper proposes iFusion, a novel CTR framework that reformulates interest fusion as a conditional generation process. The motivation of generative interest fusion is interesting. The proposed iFusion integrates long-term and short-term user interests through a denoising diffusion model, enhanced by a Disentangled Classifier-Free Guidance mechanism and an AutoRegressive Denoising Network. Experiments on several datasets demonstrate the effectiveness of the proposed iFusion.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper provides a well-motivated perspective by viewing interest fusion in CTR prediction as a conditional generation problem. This generative reformulation is conceptually appealing and offers a new way to understand how long-term and short-term user interests can be dynamically integrated.\n- The proposed framework introduces a diffusion-based fusion mechanism equipped with a Disentangled Classifier-Free Guidance (DCFG) and a Mixture AutoRegressive Denoising Network (MARN). Overall, these two designed components seem reasonable to implement a stable denoised diffusion process.\n- Experiments on both benchmarks and online tests verify the effectiveness of the proposed iFusion.", "weaknesses": "- While the idea of reformulating interest fusion as a conditional generation process is interesting, the paper could better highlight its concrete technical contributions. The description of the proposed modules (DCFG and MARN) is somewhat difficult to follow, and their precise roles or advantages over standard diffusion variants are not always made explicit. A clearer explanation or ablation focusing on the novelty of each component would strengthen the paper.\n- The diffusion process is inherently time-consuming, which is a critical concern in CTR prediction scenarios where latency is a major constraint. Although the paper mentions a consistency loss to accelerate inference, it lacks a detailed discussion or quantitative analysis of complexity, inference time, and deployment feasibility.\n- The experiments mainly use a simple DNN backbone for CTR prediction. To fully demonstrate the generality of iFusion, it would be valuable to show results when integrating with more advanced or widely used architectures, such as DeepFM, DCN, or AutoInt, to confirm the robustness and adaptability of the proposed framework.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ebHu35l2Q8", "reviewer": "Reviewer_qLFV", "summary": "This paper presents iFusion, a novel diffusion-based generative framework for click-through rate (CTR) prediction, addressing the problem about fusion of long-term and short-term user interests. Unlike traditional methods relying on late fusion and linear assumptions, iFusion reformulates interest fusion as a conditional generation process. The paper introduces two key components: DCFG to separate core preferences from transient fluctuations during generation and MARN to model short-term interest evolution in an autoregressive manner. Extensive empirical evaluations on public and industrial datasets (including online A/B testing) demonstrate that iFusion significantly outperforms state-of-the-art baselines.", "questions": "1. What is the long-term and short-term encoder the method is using? Are the encoders of LTI and SSI (in Figure 2 (a)) essential to the final performance? There seems no analysis or experiments on the encoder part.\n2. For the online A/B testing, what is the baseline model? Is it a traditional sequential RS model or a diffusion model?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a generative diffusion-based approach for interest fusion, which is a novel perspective in CTR modeling. Reformulating fusion as a conditional denoising process is conceptually strong and addresses key limitations of deterministic fusion.\n2. The paper provides rigorous theoretical analysis to justify the design of DCFG and MARN.\n3. The experiments span four datasets (public and industrial) and include online A/B tests. The reported AUC improvements and real-world CTR gains are substantial, supporting the practical value of the model.", "weaknesses": "Please refer to the Questions part.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hAJosN7aaM", "reviewer": "Reviewer_h94K", "summary": "This paper presents iFusion method for CTR prediction. Instead of simply combining long-term and short-term user interests with methods like attention, it uses a diffusion model to generate a new, fused interest representation. The generation process starts with the long-term interest and is guided by the short-term behaviors, effectively denoising and integrating the signals. The authors introduce two key components: DCFG to separate core interest from noise, and MARN to process sessions sequentially.", "questions": "Q1: The design of the DCFG mechanism is very insightful, drawing an analogy between AvgPool/Attention and low-pass/high-pass filters to disentangle core preferences from transient fluctuations. This is a strong intuition. Could you provide more empirical evidence to support this analogy and its effectiveness? For instance, have you measured the cosine similarity between the guidance gradients produced by the core preferences (g_cp) and transient fluctuations (g_tf) during training to verify their approximate orthogonality?\n\nQ2:The consistency loss is key to improving efficiency. To better understand the performance-efficiency trade-off, could you report the performance of the full iFusion model *without* the consistency loss but using multiple inference steps (e.g., 10 or 50 steps)? This would help quantify how much accuracy, if any, is traded for the significant speed-up.\n\nQ3:In the analysis of zero-data scenarios (Appendix N), you mention that iFusion can infer stable interests even when long-term historical data is missing. Could you provide more intuition on what the plausible interest points generated by the model look like in such cases? For instance, does it tend to generate representations corresponding to popular items, or does it leverage the limited short-term signals more heavily to make a best guess?\n\nQ4:The result in Figure 4a is critical, showing that a single inference step with a cosine schedule achieves the best performance on the industrial dataset. This is vital for practical deployment. Did you observe a similar trend on the public datasets (e.g., Amazon, Taobao)? Is one-step inference a generally optimal strategy for iFusion, or is its effectiveness dependent on the scale and distribution of the data?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The core idea of treating interest fusion as a conditional generation problem is a leap forward. It offers a way to handle challenges like misaligned feature spaces and noise.\n- Both DCFG and MARN are well-motivated solutions to specific problems in this new paradigm. DCFG's approach to disentangling signals is interesting , and MARN's autoregressive design is well-justified by theory.\n- The authors address the slow inference of diffusion models using a consistency loss, enabling effective one-step generation. The reported minimal increase in online latency makes it a practical solution, not just an academic one.", "weaknesses": "- Justification for DCFG could be stronger. The analogy of using pooling as a low-pass filter and attention as a high-pass filter is intuitive, but a small empirical study (e.g., visualizing gradient directions) to support this \"functional orthogonality\" claim would make it more concrete.\n- The mixture in MARN is unclear. The paper explains the autoregressive part well, but the role of mixture in the name is not clearly defined. A brief clarification would be helpful.\n- Generalizability of one-step inference. The paper shows that one-step inference is optimal on their industrial dataset. It would be great to confirm if this finding also holds for the public datasets, which would strengthen the claim's generality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)