On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
Authors: Wenyu Mao
Jiancan Wu
Guoqing Hu
Zhengyi Yang
Wei Ji
Xiang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Models
Recommender System
Preference Optimization
Primary area: deep_learning

Abstract: Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with a multi-step denoising process. However, the multi-step process relies on discrete approximations, introducing discretization error that creates a trade-off between computational efficiency and recommendation effectiveness.
To address this trade-off, we propose TA-Rec, a two-stage framework that achieves one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning. Specifically, to improve the efficiency without sacrificing the recommendation performance, TA-Rec pretrains the denoising model with Temporal Consistency Regularization (TCR), enforcing the consistency between the denoising results across adjacent steps. Thus, we can smooth the denoising function to map the noise as oracle items in one step with bounded error. To further enhance effectiveness, TA-Rec introduces Adaptive Preference Alignment (APA) that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps. Extensive experiments prove that TA-Rec’s two-stage objective effectively mitigates the discretization errors-induced trade-off, enhancing both efficiency and effectiveness of diffusion-based recommenders. Our code is available at https://github.com/maowenyu-11/TA-Rec.

On Efficiency-ffeettivensss Trade-off of Diffusion-based Recommenders
Wenyu Mao', Jiancan Wul Guoqing Hu', Zhengyi Yang', Wei Ji', Xiang Wang'
University of Science and Technology of China
Institute of Dataspace, Hefei Comprehensive National Science Center Nanjing University
Abstract
Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with multi-step denoising process. However, the multi- step process relies on discrete approximations, introducing discretization enror that creates a trade-off between computational efficiency and recommendation effectiveness. To address this trade-off, we propose TA-Rec, a two-stage framework that achieves one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning. Specifically, to improve the efficiency without sacrificing the recommendation performance, TA-Res pretrains the denoising model with Temporal Consistency Regularization (TCR), enforcing the consistency between the denoising results across adjacent steps. Thus, to map the noise as oracle items in one step with bounded error. To further enhance effectiveness, TA-Rec introduces Adaptive Preference Alignment (APA) that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps. Extensive experiments prove that TA-Rec's two-stage objective effectively mitigates the discretization errors-induced trade-off, enhancing both efficiency and effectiveness of liffusion-based recommenders. Our code is available at https: // thub. com/ woom/mawwenyu-ue 11/TA- R/T.-Re.
smooth the denoising function
Introduction
Diffusion models [1-3] have recently shown strong potential in generative sequential recommendation [4-6, 4, 7], owing their remarkable ability model complex distributions of user behaviors and generate oracle items that best match user preferences. At the core of diffusion-based sequential recommendation [4, 5, 7] is framing theoretical continuous process [2, which gradually recon- structs the oracle items from random noise. Typically, such thcoretical continuous process (e.g., Stochastic Differential Equations (SDE) [2]) realized by multi-step discrete approximations [8-11] (e.g., Denoising Diffusion Probabilistic Models (DDPM) [1]) for practical implementation: they add noise the ground- truth next items in the forward process and then denoise it step by step in the reverse process with denoising models, guided by the interaction history [12].
While achieving success, this discrete approximation inherently introduces discretization errors [13] between the practical reverse trajectory (i.e., the dashed line of discrete DDPM in Figure 1a) and the theoretical trajectory (i.e,, the solid line of continuous SDE in Figure 1a). Such discretization error is primarily caused by high-order truncation in numerical solvers and accumulates across the multi-step reverse process. When approximating SDE [2] with finite steps T, discretization errors grow T decreases [14, 15], creating an efficiency--ffectiveen trade-off: increasing the number of steps T
*Corresponding author: wujcan gmail.com xiangwang@ustc.edle
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Inference Time Comparison
Performance Timesteps
5.0
1.00
exrores (112)) T4. emor
SA5itec
Rux
Z11
fonward
Dreamfie
PDRe:
4.5
Wocchoose
0.35
Nocog20 (2hihuu
Xxc
10'
4.0
-0.40
Time
101
3.5
3.0
X
10a
0.75
Yoodhosse
Dataset
Total number
(a) The discretization error.
(b) Inference time comparison.
(c) Effectiveness and efficiency.
Figure 1:
Overall motivation for our work, including the existence of discretization error, high inference costs for diffusion- recommenders, and the effectiveness and efficiency trade-off.
(typically 1,000 steps) for diffusion-basee recommenders improves alignment between generated items and user preferences but incurs substantial inference overhead, while aggressively reducing total steps T accelerates generation at the cost of amplified discretization error and misalignment. As shown in Figure 1b, compared with traditional recommenders (e.g., SASRec [16] and PDRec [17D, the diffusion-based DreamRec [4] requires 1,000 steps to achieve comparable performance, significantly slowing inference. Conversely, reducing 7 significantly boosts acceleration yet leads to notable performance drops of DreamRec evidenced by a significant NDCG drop from T = 2, 000 T on different datasets in Figure Ic.
To resolve the trade-off, we propose TA-Rec, a two-stage framework that realizes one-step generation by smoothing the denoising function in pretraining stage and reduces the trajectory deviation by optimizing denoising models with user preferences in tinettunig stage. Technically, in the pretraining stage, TA-Rec proposes temporal consistency regularization (TCR) to enforce consistency between denoising representations at adjacent timesteps, smoothing the denoising function [18] to map noise oracle items directly [15, 14] with bounded error. This could improve diffusion models' efficiency in one-step generation without compromising the recommendation performance. In the fine-tuning stage, TA-Rec introduces adaptive preference alignment (APA) [7, 19] to mitigate the trajectory deviation and enhance recommenders' effectiveness. Specifically, APA constructs preference pairs by selecting positive and negative items from the interaction space. Recognizing that the denoising trajectory from noise to oracle items inherently varies with both user preferences and noise magnitude, APA dynamically calibrates alignment strength according to both the similarity between preference pairs and the time steps of the noisy input, optimizing denoising models in more refined manner. Notably, when the preference pair is hard distinguish and the noisy input has large noise degree, APA strategically reduces optimization strength to avoid overfitting to ambiguous preferences and random noise.
Theoretical analysis demonstrates that TA-Rec has bounded error despite accelerated generation and achieves more accurate alignment, thereby enabling reliable recommendations matching user preference. Empirical results across multiple real-world benchmarks (i.e., zhihu [20], KuaiRec [21], and YooChoose [22]) show that our approach achieves 100x faster generation compared to leading diffusion-basse recommenders (e.g., DreamRec [4]) while simultaneously improving recommendation performance by 10%, achieving both higher efficiency and higher effectiveness.
2 Realated Wrok
Diffusion-based Recommenders [4, 6, 23-25] have emerged as a promising alternative to conven- tional sequential recommenders by modeling the complex user behavior distribution and generating next items step by step to match user preference. Existing methods have explored various applications of diffusion models in recommendation systems, including next-item generation through denois- ing process guided by historical sequences [4, 5, 23], augmentation of sequential recommenders [26, 17, 27], and improvement of robustness [28] against noisy feedback [29, 30]. Our work addresses the issue of discretization error [13] in the reverse process for next-item generation paradigm, which fundamentally impacts the efficienc--fffectiveeee trade-off that currently limits diffusion-bain based recommenders' practical deployment.
Accelerating Diffusion [14, 31-34] addresses the computational bottleneck inherent in the multi-step generation process of diffusion models. Recent advancements fall into two primary categories: Trining-frree numerical solvers [14, 35, 36] and training- model distillation [37-40]. The former

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optimizes numerical solvers to serve as faster samplers during the reverse process, reducing inference steps to 20-50 without tuning the original denoising model [14, 41, 42]. For instance, DPM++ [43] leverages adaptive high-order solvers to approximate the continuous SDEs [2], achieving faster generation via optimized numerical integration. To achieve more aggressive acceleration (1-4 steps), the latter approach focuses on tuning the denoising model through knowledge distillation. Specif- ically, consistency models [15, 44] distill pre-trained diffusion models into single-step generators by enforcing self-consistency of the ODE trajectory. However, these methods may either rely on well-pretrained models or a deterministic ODE process, limiting their applicability to recommenda- tion tasks due to the lack of unified recommendation benchmarks and the dynamic nature of user preferences. Instead, our work achieves one-step generation of diffusion seffusion-based recommenders by designing temporal consistency loss for the stochastic DDPM process.
Aligning Diffusion Models [19, 45-47] aim to optimize diffusion models using human preference data, an area that remains relatively underdeveloped compared to preference optimization in large language models (LL.ss). Current approaches often adapt existing LLM preference optimization algorithms to diffusion models. For example, Diffusion-DPO [19] takes the DPO loss [48] from LLMs and adapts it on diffusion models directly to improve image generation based on user preference. DSPO [47] aligns diffusion models with human preferences by distilling the score function of preferred image distributions into the model's pretrained score functions, leveraging score matching. Furthermore, Diffusion-NPP [46] approaches preference alignment by training additional model to specifically model negative preferences. To enable alignment at a more fine-ge ane-ggrined level, our work designs adaptive preference optimization algorithm that aligns the generation of diffusion-bart based recommenders according to both timesteps and pair similarity.
3 Preliminaries
3.1 Diffusion-based Sequential Recommendation
In sequential recommendation tasks, we represent a user's historical interaction sequence chrono- logically as x!:8 I:N-1 [x' xx '1 in the embedding space, where each x" Rd denotes the embedding of the user's n-th interacted item. The next item to be interacted with is denoted as x (x for simplification). The goal of diffusion-based sequential recommendation is recover the oracle item [4] that best aligns with users' preferences from noise, guided by user interaction history. Following [4, 23, 71. in the forward process, Gaussian noise is added to the next item q(x, xx) = (xx: Vā,x, (1 āzz I), where [ai,..., ,T] denotes the noise scale, t E [I,...,] 7] denotes the timestep. During the reverse process, diffusion models recover the next item x from noise under the guidance g through a multi-step denoising process. To optimize the denoising model, the training loss for diffusion-baan based recommenders can be formulated [4, 7]:
Lau Ex.g.' [l,f g.
x1(]] -
(1)
where g is the guidance signal extracted from users' historical interaction sequences x x1:N-1 with a Transformer following [4, 71. fe(-) is denoising model parameterized by MLP. reconstructing the target item x as &o directly under the guidance g, as shown in the green part of Figure 2. Commonly, classifier-free guidance paradigm [12] can be utilized by replacing the guidance g with a dummy token 4 at probability p. allowing for the integration of conditional and unconditional training. During inference, the pure Gaussian noise Xy (O, I) serves as the input, then the denoising model fo(-) denoises items as &o under the guidance The iterative reverse process for generation is:
0z
3r &
00 (1 ā X, + v BiR, z z~N(
X
1 at
After generating the oracle item Xy step-by-step, we calculate the dot product between x, and each candidate item in the corpus, then recommend K items having the highest similarity scores.
3.2 Direct Preference Optimization on Diffusion Models
Direct Preference Optimization (DPO) [49-51] is reward-model method that aligns LLMs with human preferences via supervised loss derived from pairwise comparison. To adapt DPO [48] to the diffusion model [19, 47, 46], the key idea is to increase denoising models' likelihood of preferred
3

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Pretraining stage: one-step generation
Fine-tuning stage: preference optimization
item
noise
positive item
Forward
Xx
X
X
fo(%) 8:
Adeptive alignment
Denoising
Temporal consistancy
Feot
(Apa(Ay(t, d)
PreR Bill (x 1o By 1)
negative item
XT
X
Forward
(x 8,
x
Diffusion reconstruction
Transformer
Ay(t, Amma 1/T)(1
Figure 2: The overview of our proposed TA-Rec framework, which alleviates the efficiency and effectiveness trade-off of Liffusion-basee recommenders with two stages. In the pretraining stage, TA- Rec achieves one-step generation with temporal consistency regularization. In the fine-tuning stage, TA-Rec adopts an adaptive coefficient (t d) to align the generated item with users' preferences.
generation while decreasing that of dispreferred ones. We assume the pairwise preference data as D = -=+xxxx,, x"), where x is preferred over x Following [19], the DPO training loss on diffusion model can be formulated as:
pe (x:²
1:
po
x
X,
g)
DDiffusion-DPO = -E log Ag log
Xo log
(3)
Peef (xx²
x
Pref
x
X x
g)
where xt_ q(x," x ) xi q(x, x ) g the guidance of denoising model, Prof represents
the reference distribution, AØ is hyperparameter which controls preference optimization strength.
4 Method
In this section, we present our proposed TA-Rec framework, two-stage approach addressing the discretization error-induced trade-off in iuffusiio-based recommenders. The overall framework is illustrated in Figure We first define the discretization error of diffusion models in Section 4.1. We then detail the Temporal Consistency Regularization (TCR) during pretraining in Section 4.2. Finally, we describe the Adaptive Preference Alignment (APA) during fine-tuning in Section 4.3.
4.1 Definition Discretization Error Diffusion Models
The continuous reverse process of the diffusion model can be described by the SDE process [2] mathematically. Formally, we have: dx = [f(x, s) - g(s)² log (x)] ds + g(s)dū, where f()) and g(-) are drift and diffusion coefficients, is reverse-time Brownian motion, and (x(x) is the marginal distribution at time (s E [0, 1)). In practice, the theoretical continuous SDE is
approximated by discrete numerical solvers (e.g., Maruuaaaa [52], first-order solver) with T
steps (step size At 1/T):: X = X; + [
g 2--22 2 logp:] At giz (O,
Definition 1 The deviation between the continuous process and its discrete approximation is the discretization error Edise: Edsac (t) = Ex~pre: [xx()) xel|g]1 where X(x) is the theoretical SDE
solution at time s = t. At, X₁ is the discrete approximation at step t, t [1,..... [1....,T].
Such discretization error primarily stems from local truncation error in solvers' numerical integration [53, 54] and is accumulated globally during the propagation across multiple steps the reverse process [55, 56), where Edise (0) ~ O(A(). Thus, reducing reverse steps T' (ie., increasing A/) will amplify Edisc and cause trajectory deviation, introducing the efficiency and effectiveness trade-off
Pretraining Stage: Temporal Consistency Regularization (TCR)
In recommendation systems, the multi-step reverse process introduces high computational costs, failing to meet the low-latency demands of large-scale online deployments. To improve efficiency of diffusion-based recommenders, we aim to realize one-step generation, which maps the noise the oracle items directly. Specifically, TA-Rec proposes temporal consistency regularization (TCR) to smooth the denoising function of diffusion-based recommenders in the pertaining stage, shown

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in the left part of Figure 2. In addition to the reconstruction loss in Equation (1), TCR enforces consistency between consecutive timesteps to smooth the denoising function fo(-) with Lipschitz continuity [18]. For adjacent noisy representations x, and x,-1 (obtained via the forward process ((x,|x) = (xx: Voix, (1 - T) as introduced in Section 3.1), the TCR loss can be defined as:
LICR = Ex..g. (x), g, t) - (x 1)]1
where g is the guidance signal extracted from the historical interaction sequence x I:N-1 with Transformer, as introduced in Section 3.1. The total loss, which jointly optimizes the denosing model with reconstruction loss in Equation (1) and TCR loss in Equation (4), can be formulated as:
fie = Lant + A LICR.
(5)
where A is the strength that balances reconstruction accuracy and denoising smoothness. Upon optimizing Cpre. we can obtain the smooth denoising function fx( g, t) that consistently projects the noisy representation X at any step on the trajectory to the target item x. Thus, the pure noise XT (O, I) can be mapped by the smooth denoising function as oracle items Xo directly with one-step generation, eliminating the need for iterative reverse steps. Below, we justify that such accelerated generation has a bounded error.
Theorem 1 (Error Bound for One-Step Generation) We assume that: (i) The smooth denoising
function fo (xt, g, t) satisfies Lipschitz continuity with constants > 0: [/ //tx g, 31 At) fo(Xx,/ Ar. g, s2/ 2 I L|s1 - s2) for any time $1, E [0, 1]. (ii) For any step E [L.....]
and any time s E [tAt, (t+ 1)) there exists a constant C such that the following smooth condition
holds lfo(X./At. s/ At) - fe (Xe, t)l S. (' g, t + 1) fo (x,, g, s.t)ll2. Then, since we minimize Lpres the error of one-step generation of x from any s bounded as follows:
sup [lf / g, s/At) x1|2 0 (At). s,x
(6)
Thus, the error of one-step generation remains bounded by the global discretization error O(Al) in multi-step reverse process, validating that acceleration will not amplify trajectory deviation or disrupt the recommenders' effectiivness. The full proof provided in Appendix A.
4.3 Fine-tuning Stage: Adaptive Preference Alignment (APA)
After improving the efficiency as detailed in Section 4.2, we further enhance diffusion-basee recom- menders' effectiveness by aligning the generated items with users' preferences closely, mitigating the trajectory deviation caused by the discretization error. Technically, TA-Rec introduces adaptive preference alignment (APA) in the fine-tuning stage to optimize the denoising model fo(.) with users' preference pairs. To align the denoising trajectory with user preferences in more refined manner, APA designs an adaptive coefficient Ag. which controls the strength of preference optimization (as introduced in Section 3.2) adaptively. For each sequence x 1::N-1 we randomly sample the negative item x from the batch, constructing the preference pair with ground-truth next item x (i.e., x). Since denoising from noise to oracle item is relative to user preference and the noisy degree of xt, the optimization strength Àg adapt at both pair- and step-wise For preference pair (x+ x ). we define the similarity between positive and negative items as d(x" xx = cosine(x* x ), where cosine denotes the cosine similarity. Thus, the alignment strength À s) can adapt according to timestep f and pair similarity d(xt,,'' ))
Xs(1, d) = Anose (1-+)+(1-dxt+x)))
+1 (1-d(xt,<x d(xt,x)))
where Ahose controls the base alignment strength, we set it as 1/2. When the pair similarity dxxt, x-) and the step / large (i.e., the preference pair is hard to distinguish and x, has a large noisy degree), the optimization strength Ag(t, d) can be small, which can avoid overfitting to ambiguous preferences and random noise. The APA loss integrates this adaptive strength At d) into the DPO loss of Diffusson as introduced in Equation (3), and we have:
CAPA = -E loyo (Aa(k.d) log
Po (xt x++11)
po (x5 xx+111) g)
X g) )
Ax(1, d) log
(8)
Prof (x. x g)
Pret (x,
5

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where x and are noisy representations xt and x (obtained by q(x{(x) N(x; v /xx, (1 d,)[)), Pref denotes the reference distribution, which can be initialized using the distribution from the pretrained denoising model. The Transformer model to extract guidance signals g is frozen in the fine-tuning stage. Similar to [19], the loss to optimize denoising model fo(.) can simplified as:
CAPA IP log (-)p(t, (11oo(xt.B. :. xt 112 [x] ., - -x00x-1p xx12
(Ilfo (x4 g.tt) x [[ (x, 1...g.1
where fier(-) is the reference model initialized by the denoising model from the pertaining stage. Below, we justify that the pair- and step-aware adaptive coefficient > d) allows the denoising model to align the generation with user preferences more closely.
Theorem Suppose that the preference of fo and fid have the following relation: : 8, t) I
[mg(X; 1) S Solxi (.xxi, .,.1) S <k -g; 18:1) ,g, where k is constant and k S e. Then with increasing Xg. the parameter update of 0 from preference optimization becomes more aggressive, i.e., the norm of gradient VVoLoiflssoo-DPoll x Xg.
Theorem justifies that the denoising model's parameter 9 updates more slowly when the coefficient (t, d) is small. The proof is detailed in the Appendix A.2. When the pair similarity d(xt, xx) and the step t is large (i.e., the preference pair is hard to distinguish and X has a large noisy degree), can have small optimization strength (t, d). Thus, parameters 0 of the denoising model can update more slowly, which can avoid overfitting to ambiguous preferences and random noise. And vice versa. Such adaptive strategy allows the denoising model to align the generation with user preferences more closely in more refined manner.
4.4
Overall Pipeline
As shown in Figure 2, the pipeline of TA-Rec begins with pretraining the denoising model under Temporal Consistency Regularization (TCR) to enable efficient one-step generation. Given a user's interaction sequence x xI:N-1, we first extract guidance signals g using Transformer encoder.
The adjenct noisy representations x, and x are generated via the forward process ((xx(x) = N(x;; Vāyx, (1 du )I), where is the ground-truth next item. The Transformer and denoising model is then optimized using a joint loss Lpe = Lait + de LICR. In the fine-tuning stage, Adaptive Preference Alignment (APA) fine-tunes the denosing model fo(.) (.) to better align generated items with user preference. For each sequence, we construct preference pairs (x+,x)) items that users have not interacted with as negatives. The alignment strength (a d) is dynamically adapted based on both the timestep i and pair similarity d(xt, x-) as detailed in Equation (7). The APA loss CAPA optimizes the denoising model with the guidance encoder frozen.
x-) ) by randomly sampling
During inference, the model generates recommendations x!:N-1 obtain guidance g and sample noise Xy ~ (O( I) input. Then, generate oracle items with the denoising model: Xy = fe(x,, g. T). The final recommendations are obtained by ranking candidate items via dot product with generated items Xg. The algorithm of the pertaining stage, fine-tuning stage, and the inference phase of TA-Rec are presented in Appendix B.
single step: given a user's history
5 Experiments
In this section, conduct extensive experiments to evaluate how TA-Ree addresses the trade- off by answering the following questions: RQI: How does TA-Rec perform in the sequential recommendation tasks compared with leading baselines? RQ2: What are the contributions of TCR and APA in TA-Rec? RQ3: How sensitive is TA-Rec to the strength of consistency regularization and preference optimization? RQ4: How efficient is TA-Rec compared traditional recommenders and diffusion-basee recommenders? RQ5: Can TA-Rec generalize to multi-step reverse process and different pretrained diffusion-based recommenders?
5.1 Expermental Settings
Datasets. We adopt three common datasets in sequential recommendation tasks to conduct the experiments, including Yoochoose [22], KuaiRec [21], and Zhihu [20]. To process the dataset,

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exclude items with fewer than five interactions and sequences shorter than 3 interactions to mitigate cold-start issues following [4]. Then, we sort all sequences chronologically and split the data into training, validation, and testing sets in an 8:1:1 ratio to prevent data leakage. The dataset statistics are provided in Appendix C.I.
Baselines. We evaluate the performance of TA-Rec against multiple leading sequential recommenders thoroughly, including:
Traditional Recommender: GRU4Rec [57]. Caser [58], SASRec [16], Bert4Rec [59], and CL4SRec [60] predict next items by calculating the similarity between candidate items and the interaction sequences, which are modeled using GRU and Transformer architectures.
ceffusion-baset Recommender: DiffRec [6], DiffuRec [5], DreamRee [4] leverage diffusion models to formulate the adding noise and denoising process recommenders, generating item embeddings or item scores step by step.
Preference-based Recommender: DiffuASR [26]. PDRcc [17]. DimeRec [61]. PreferDiff [7]. DiffuASR [26], PDRee [17] employ diffusion models to generate augmented items that enrich user preference representations. Mcanwhile, DimeRec [61] and PreferDiff [7] directly generate recommended items using diffusion models, enhanced by multi-interest extraction and preference pair optimization, respectively.
The detailed explanation for baselines is presented in the Appendix C.2
Implementation Details. Following DreamRec [4]. the historical interaction sequence length set to 10, with sequences containing fewer than 10 interactions padded using padding to- ken. Item embeddings are dimensioned at 256 for the Zhihu dataset and 64 for the KuaiRec and Yoochoose datasets. The learning rate during the pretraining stage is tuned within the range of (0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005). The timesteps T for forward process are varied across 500, 1, 000, 2, 000). The hyperparameter of A is tuned across the range [0.1,..., 1]. The experi- ments are implemented with Python 3.9 and PyTorch 1.0.1 on the Nvidia GeForce RTX 3090. We employ widely used metrics in sequential recommendation: hit ratio (HR @20) and normalized discounted cumulative gain (NDCG@ @ 20) for evaluation. Each method tested five times, with the average performance and corresponding standard deviations reported in the tables.
5.2 Main Results (RQ1)
To answer RQ1 and validate the effectiveness of TA-Rec, we present the overall recommendation performance on three datasets in Table 1. To implement, all the methods that leverage diffusion models are based on DDPM [1] with multiple reverse steps, while PreferDiff is based on DDIM [35] for acceleration (10-20 steps) according to the original setting in [7]. Our proposed TA-Rec consistently outperforms leading baselines of three categories across datasets, demonstrating its superiority in recommendation effectiveness. As shown in Table 1, TA-Rec achieves the best performance under both HR@ 20 and NDCG@2 20 metrics, with significant improvements over the strongest baselines (7. 1..441%...222
YooChoose, 1.55-7..54% on Zhihu). DreamRec [4] demonstrates competitive performance but incurs high computational costs due to its 1. st000-step generation process. PreferDiff [7] demonstrates excellent performance by integrating BPR loss with diffusion reconstruction loss and enabling faster generation (10-20 steps) through the adoption of DDIM; however, still trails behind TA-Rec. The superiority of our method with one-step generation validates the success of our two-stage framework in enhancing both the efficiency and effectiveness of diffusion-based recommenders.
KuaiRec, and 7.52%.9, 99.6%%9.4%%
5.3 Abalation Study (RQ2)
To validate the respective contribution of TCR and APA in TA-Rec, we conduct ablation studies with experimental results presented in Table Specifically, we design several variants for TCR, where "DDPM" represents adopting the DDPM [1] paradigm to generate items step-by-step (1k steps) to recommend, guided by user interaction history. "DDIM" refers to leveraging DDIM [35] to accelerate the multi-step reverse process to just 10-20 steps. "w/o TCR" denotes fine-tuning denosing models from pretrained DDPM without TCR loss, "w/o APA" denotes pretraining denosing models without the fine-tuning stage. "w/o t", "w/o d", "w/o td" represent conduct preference alignment without
7

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Table Overall performance of different methods of sequential recommendation. The best score and the second- best score are bolded and underlined, respectively. The last row indicates the performance improvements of TA-Rec over the best-perforning baseline method.
YooChoose
KuaiRee
Zhihu
Methods
HR@20
NDCG@20
HR 20
NDCG@20
HR @
NDCG@
GRU4Ree
3.89=0.11
1.6220.02
0.32=0011
1.23:1.08
1.78
0.6710.03
Caser
4.06=2.12
1.8820.09
19
1.07:0.07
1.57=0.05
0.59
SASRec
3.79=a.0x
1.71
4.02=0.00
1.79+t.10
+0.5+
0.77++0.03
Bert4Rec
4.96=a.05
2.05#00003
3. 0..7
1.73+0.00
2.01
0.72+
CLASRec
4.67
2.1220.01
4.43-0.07
2.66#2.03
2.10
0.76+0.04
DiffRec
0.33=20.0
1.84
3. 0.00
1.77
1.82 10.03
0.65
DiffuRec
1.63=0.03
2.23=0.04
1.50
3.40#n.06
2.23
0.81
DreamRec
1.78=0.06
2.23#0.02
5. 5.16=0.05
1.11#0.02
2.26=0.07
0.79
DiffuASR
1.48=0.03
1.92#0.02
0.53
3.30#0.03
2.05
0.71+
PDRec
5.43:20.02
3.08=20.2
4.48
3.68
2.12:10.04
0.76:10.03
DimeRec
5.33=0.03
3.8620.00 08
4.1720.04
3.64220.05
2.0620.02
0.78
PreferDiff'
5.74+0.07
3.07#00.8
4.87=0.02
3.81+0.07
2.22 60.03
0,83++o.00 H0.01
Ours
6.15
4.0620.02
5.40+0.02
4.42:00.04
2.43
0.91 0.0
improv.
7.14%
31.82%
4.65%
7.54%
7.52%
9.64%
Table 2: Ablation Study for TCR and APA.
The best performance is bolded.
YooChoose
KuaiRec
Zhihu
Methods
HR@20
NDCG@20
HR@20
NDCG@20
HR@20
NDCG@20
DDPM
5.68
3.81 +0.03
3.23+0.02
4.23+t0.06
2.33++0.04
0.86#t0.08
DDIM
5.3420.04
3.7510.03
3.0521.02
3.91
2.3820.00
0.81
w/o TCR
3.7220.02
3.82+0.03
3.2410.02
4.2310.04
2.3922.05
0.87=10.01
w/o APA 5.89
3.8220.05
5.3310.06
4.30+10022
2.404
0.8920.05
w/o td
6.10
4.04
5.30
4.39:40.05
2.28
0.87:1.002
w/o d
6.11
4.05
3.38
4.4010.03
2.38
0.8940.02
w/o
6.12#0.03
4.04+0.01
5.39+0.02
1.41+0003
2.35+t.01
0.89+0.04
Ours
6.15
4.06
5.40
4.42=0.01
2.43
0.91
considering step or pair similarity s or both. As shown Table 2, all the variants surpass the DDPM- or DDIM-based recommenders, demonstrating the significance of TCR and APA. Additionally, the superiority of "w/o d",, "w/o t" over "w/o td" validates the necessity of pair- and step-wise adaptation for APA. More analysis for ablation study is detailed in Appendix D.I.
5.4 Sensitivity Analysis (RQ3)
We investigate the sensitivity of TA-Rec the hyperparameters A, (controls the weight of TCR loss in pertaining stage) and Ag (controls the strength preference alignment in fine-tuning stage). The results of 7e are shown in Figure 3, where "onestep" and "multistep" refer to TA-Rec using one-step generation and its extension in a multi-step setting (5-10). We can observe that the performance peaks when the regularization strength \ [0.4, 0.8). balancing consistency smoothing and item reconstruction. Excessive regularization (A, > 0.8) suppresses oracle item modeling, while weak regularization (Ae < 0.3) fails mitigate discretization errors. The results for λ are presented in Figure 4, where "fix one", "fix mul", "Our "Our_sooe".. and "fix_one" fixed X or our adaptive (t, (, d) with one or multiple steps-generation, respectively. The curves of "fix_one" and "fix_ "_ll" fluctuate as Ag changes, highlighting the significance of ' in performance optimization. The curves of "Ours_one" "one" and "Ours_man mul" lie above those of fix__one" and "fix fix_"""", validating the superiority of our design of adaptive coefficient As(t, d) in aligning user preference.
one" refer to fine-tuning TA-Rec with

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Zhihu
KuaiRec
Yoochoose
0.500
4.55
4.05
0.925
4.50
4.00
0.900
4.45
ADC6970
3.55
4.46
3.40
4.35
3.85
0.850
4.30
3.80
muil step
ohestep
4.25
mis mullistep
orestep
3.75
onestep
mulistep
0.800
4.20
3.70
0.2
0.4
0.6
0.8
1.0
0.4
0.6
0.8
1.0
0.2
0.6
1.0
Ap
Ae
Figure 3: Performance of TA-Rec different Ao demonstrating the sensitivity of TA-Rec the strength of consistency regularization.
Zhihu
KuaiRec
Yoochoose
4.08
0.92
0.90
NDCGG20
NDCG820
0.88
3.86
fxg mul
Ours_onc Oursmmu
43
fxx mml
Oumsmul
figmul
0urimm
Ourz
Figure 4: Performance of TA-Rec on different λą, demonstrating the sensitivity of TA-Rec on the strength of preference alignment and the superiority of our Adaptive preference alignment.
5.5 Computational Resource Comparison (RQ4)
To answer RQ4, we compare the training and inference efficiency of TA-Rec with traditional recommenders (e.g., SASRec [16]) and diffusion-bart based recommenders (DreamRec [4] with Ik steps and PreferDiff [7] with 10-20 steps). The time costs are presented in Table 3. We observe that the computational complexity of TA-Rec for training each epoch comparable that of other diffusion-basedl and traditional recommenders using the same sequence encoder (i.e., Transformer [62]). Moreover, by employing TCR to realize one-step generation, we significantly enhance the efficiency of TA-Rec during the inference phase. As result, TA-Rec substantially reduces inference time compared to DreamRec and PreferDiff, achieving performance levels similar to SASRec.
Table 3: Running time comparison of TA-Rec and other recommenders that use the same sequence
encoder on three datasets.
YooChoose
KuaiRec
Zhihu
Methods
Traning
Infereneing
Training
Inferencing
Training
Inferencing
SASRec
01m18s
00m 06s
07s
00m
00m 12s
00m Ols
DreamRec
01m 19s
23m 14s
02m 23s
28m 02s
00m 12s
05m 04s
PreferDiff
01m 18s
00m
22s
00m
00m
03s
Ours
01m 18s
00m 06s
02m 21s
00m 07s
00m 12s
00m Ols
The analysis and experiments conducted for RQ5 are detailed in Appendix D.2, demonstrating that TA-Rec generalizes effectively in multi-step settings (1-5 steps) and that APA can enhance the performance of other diffusion-bart based recommenders using DDPM [1] or DDIM [35] backbones.
Conclusion
In this paper, we propose a novel two-stage framework, TA-Rec, address the critical efficiency- effectiveness trade-off in diffusion-based sequential recommenders. To improve efficiency without compromising recommendation performance, TA-Rec integrates Temporal Consistency Regulariza- tion (TCR) in the pretraining stage, smoothing the denoising function to realize one-step generation with bounded error. To further enhance effectiveness, TA-Rec fine-tunes the denoising model with Adaptive Preference Alignment (APA) at both pair- and step-wise. Theoretical justification

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and extensive experiments validate that TA-Rec can enhance both efficiency and effectiveness of Diffusion- recommenders, addressing the discretization crror-induced trade-off.
7 Acknowledgement
This research is supported by the National Natural Science Foundation of China (62572449, 62302321), This research is also supported by the Fundamental Research Funds for the Central Uni- versities (WK210025006) and the advanced computing resources provided by the Supercomputing Center of the USTC.
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A Proofs
A.1 Temporal Consistency Regularization
Theorem (Error Bound for One-Step Generation) We assume that: (i) The smooth denoising
function fo fx g, t) satisfies Lipschitz continuity with constants L 0: [[ /At g, 31 At) fo(Xs//At g 52/ 22)ll2 < L|st for any time 81,52 E [0, 1l. (ii For any step E [L....,I]
and any time & E [tåt, (t++1)4r there exists a constant C such that the following smooth condition
holds (f.l../A...../////// s/A) fo(x,, g, t)ll2 S (o g, + 1) - ff(xx,, g, t))l22 Then, since we minimize Tepres the error of one-step generation of x from any is bounded as follows:
sup (x 8.8/At) x||2 = O (AI). ax
(10)
Proof 1
We estimate the error
[]Je(x./a))) 8/D1) - x112 - [//o(x./Ar 8 8/21) - f0( 8, 1)12 + (I,o(x,, g. x112 (11) = [|Jo(x./Ar:8) 8/ - fo(x,, g, 1)]l2 6.
The second term [ g,t) xl12 represents denoising model's reconstruction error, bounded by
the loss Lasss- This error inherent to denoising model, regardless of whether multi-step
accelerated one-step inference is used. Since we minimize Laiff, we have Il (x,,, g,t) t)- x||2 6, arbitrarily small and negligible. Using the Lipschitz condition, have
[x B. 8.8/ s/At) fo(xi, g: 12 I (1]ff(x(+1.8. g + 1) fo(x,, g. t12l
S LCI(L + 1) - [
(12)
O(A)).
Thus, we have sups,x (lfo(xs/At..)))) s/At) - x = O (At).
A.2 Adaptive Preference Alignment
Theorem 2 Suppose that the preference of So and fug have the following relation: s.I. frg(x] g, t) fng(X; .g. S folx; g,,) fo(x, g,t) S. k g, t)/ fre(X, :g, t), where constant and k S e. Then with increasing Xg, the parameter update of from preference optimization becomes more aggressive, i.e. the norm of gradient || DoLoiuuioon-Do x Xg.
Proof The gradient of CDifusion-Dr Dro defined as:
Woooiiiusion Droll = 1 ch VolAr),
(13)
where Ar log folx] gg.) Tno(x? gg.) fn((x, .g.))
log fa(x, .g.1]
The gradient of V &&Liiuuiio-DPO with respect
Ag is:
1 1 dadr Ageredd
V Ag Vo Diifussion DPO DPO
(1+ cAorry2 Vo(Ar).
(14)
Given the initial condition:
.gt))/f(((() ggt)
Ar = log
$ <log k < 1.
(15)
frelxt g, g)t) (n) .g.t)
Thus, we have:
1 Lede AgAredad
(1 (1+etv2rj2 vo(Ar) 20.
(16)
This implies two cases:
1. If V Doiiissiin-De 2 0, then Vo(Ar) 2 0 and V V Do DPO 2 0.
2. If V Dif < 0, then vr(Ar) < 0 and V Xs V VoLDguuion-DOO 0.
That means the norm of gradient || D.Diffusionnnoo x Ag. The parameters of update slowly when the coefficient xg is small.
21

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B Algorithm
Here we list the algorithm of TA-Rec's pretraining, fine-tuning, and inference phase in Algorithm 1, Algorithm 2, and Algorithm 3.
Algorithm 1: Pretraining stage of TA-Rec
Input: Interaction sequence x x1:N-1 next item x, hyperparameters A.. initialized denoising model fo(-) and Transformer encoder.
Output: optimized denoising model fe(-).
g Transformer(x* (x:NN Encode the guidance.
2: ~ II....
B Sample diffusion step. D. Sample Gaussian noise. &. Add steps Gaussian noise. Add f steps Gaussian noise.
3: z I)O.I)
4: X; Voxx+ + (1 -1-ā:) 0z
5: x -o
a(-1)z
6: Laif Ex.g.t [11o((x,, x1
Calculate reconstruction loss.
7: CTCR = Ex [x1sa(x;;g,1 - fo (x 1)
Calculate TCR loss. Total loss of pertaining stage
>
8: Lpre Lai + d LICR
9: Update fo(.) and Transformer encoder with LICR.
Algorithm 2: Finetuming stage TA-Rec
Input: preference pair (x* ). pretrained denoising model fa(+), guidance g. Output: optimized denoising model fo(-).
1: ~ [1.....
Sample diffusion step. D Sample Gaussian noise.
2: 2 ~ I(O,
3: x = vaixt + (1 āz)z
b. Add Gaussian noise to positive items. Add Gaussian noise to negative items. > Calculate adaptive coefficient. Adaptive preference alignment loss.
4: x, Voix + +1--ū:)) 0z)z
5: )g(t,d)= Abase ((1 4) (1 d(xt x x)))
6: LAPA + Equation 9
7: Update fo(-) with CAPA
Algorithm 3: Inference phase of TA-Rec
Input: Interaction sequence x 1: optimal denoise model fo()), Transformer encoder. Output: Oracle item embedding X
1: Xy ~V.
Sample Gaussian noise.
2: g = Transformer(x!:> !:1-1)
Encode interaction sequence as guidance.
# One-step generation
3: Xo =fo XT: g. T)
# Multi-step generation
4: for = T, -..... do
5: z _N ift > 1, else z
6:
80 = fe g,t)
7: X Var-18 Var(1-ar-
Xx v
Biz.
Reverse step step.
I-a;
1-
8: end for
9: return Xg
C Detailed Experimental Settings
C.1 Details of Datasets
Here, present the statistics of our datasets in Table 4.
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Table 4: Statistics the three datasets.
Dataset
YooChoose
KuaiRee
Zhihu
#sequences 128,468
92,090
11,714
#items
9,514
7,261
4,838
Minteractions
539,436
737.163
77,712
C.2 Detailed Baselines
Here, we detailed baseline methods, including traditional Recommender, diffusion-based Recom- mender, and preference-basee Recommender. Traditional Recommender: These methods predict next items by caleulating the similarity between candidate items and the interaction sequences, which are modeled using GRU and Transformer architectures.
GRU4Ree [57] uses gated recurrent units (GRUs) to model user behavior sequences, processing session data sequentially to predict next-item interactions.
Caser [58] applies horizontal and vertical convolutional filters capture both point-wise and union-level sequential patterns from user interaction sequences.
SASRec [16] employs self-attention mechanisms to weight historical interactions dynamically, modeling long-range dependencies in user behavior sequences.
Bert4Rec [59] adapts the bidirectional Transformer architecture with masked item prediction to learn contextual representations of user behavior sequences.
CLASRec [60] incorporates contrastive learning by constructing augmented sequence views through item cropping, masking, and reordering operations.
These methods leverage diffusion models to formulate the adding noise and denoising process in generative recommenders, generating item embeddings or item scores step by step.
Diffusion-based Recommender:
DiffRec [6] optimizes the diffusion model to predict noise in corrupted interactions, where diffusion steps progressively refine interaction probabilities.
DiffuRec [5] employs diffusion models for sequential recommendation, which adds Gaussian noise to next items while preserving historical sequences.
DreamRee [4] reformulates sequential recommendation as oracle item generation via classifier-free guidance-basee based diffusion, where encoded historical interactions serve condition signals.
Preference-hased Recommender: These methods employ diffusion models enrich user preference representations or enhance diffusion models' ability with learned user preferences.
DiffuASR [26] enhances sequential recommendation by generating high-quality pseudo sequences using a diffusion model, specifically designed to better capture and align with user preferences.
PDRec [17] leverages diffusion models to generate comprehensive user preferences on items, using strategies like historical behavior reweighting and noise-free negative sampling to enhance the representation of user preferences.
DimeRec [61] shifts the recommendation task from generating specific items generating user interests, using a multi-interest model to extract stable user preferences and a diffusion model to reconstruct user embeddings.
PrfferDiff [7] introduces tailored optimization objective for diffusion-based recommenders, transforming BPR into log-likelihood ranking objective and integrating multiple negative samples to better capture and align with user preferences
23

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Table Ablation Study for Temporal Consistency Regularization. The best performance is bolded.
YooChoose
KuaiRec
Zhihu
Methods
HR 20
NDCG@20
HR@20
NDCG@20
HR@20
NDCG@20
DDPM
5.68
3.80
5.2310.02
4.2311006
2.33:1.04
0.86.10.5
DDIM
5.3410.04
3.75:10.03
5.05:0.02
3.9110.05
2.3811.01
0.81:0.05
w/o TCR
5.72+0.02
3.82
5.24+0.02
4.23+0.04
2.39
0.87+0.01
w/ TCR
5.89+v.02
3.82
6.33
4.30
2.41+0.04
0.89+6.05
Ours
6.15+0.02
4.06#0.00
5,40
4.42
2.43
0.91
Table 6: Ablation Study for the adaptive strategies for preference Alignment.
YooChoose
KuaiRec
Zhihu
Methods
HR@20
NDCG@20
HR@20
NDCG@20
HR@20
NDCG@20
w/o APA
5.89
3.82+0.05
5.33+0.06
4.30=t.02
2.004
0.89=n.05
w/o td
6. 6.10+0.02
1.0410.04
5.36+0.04
1.39
2.28+4.05
0.87=0.02
w/o d
6.1110.00
4.05:10.04
5.38:10.0
4.40:10.03
2.38:1000
0.89
w/o
6.12:10.3
4.04:10.01
5.39
4.41:0.03
2.35
0.89
Ours
6.15
4.0620.04
5.40
4.42:0.01
2.43
0.91
D
More Experimental Results
D.1
Detailed Analysis for Ablation Study
We present the ablation study for TCR and APA in Tables 5 and 6. From the results in Table 5, we observe that "DDPM" achieves moderate performance but incurs high inference costs with a 1 1,000-step reverse process. In contrast, "DDIM" reduces reverse steps to 10-20 but experiences performance drop (e.g., 3. 3.75% for "DDIM" vs. 3.81% for "DDPM" on YooChoose). However, "w/ TCR", which employs TCR to facilitate one-step generation through smoothed denoising function, outperforms both "DDPM" and "DDIM", underscoring TCR's s advantage accelerating generation without sacrificing recommendation performance. Meanwhile, "w/o TCR", which fine-tunes DDPM- based recommendations pretrained without TCR loss, surpasses the "DDPM" variant but lags behind TA-Rec, further highlighting the importance of TCR loss during the pretraining stage.
Additionally, as shown in Table 6, "w/o td", "w/o d',, "w/o t", and our TA-Rec all outperform "w/o APA" across the three datasets, confirming the effectiveness of APA in aligning user preferences during the fine-tuning stage. Removing step-wise adaptation ("w/o t") or pair-wise adaptation ("w/o d'") degrades the performance of our TA-Rec (e.g.. Zhihu HR @ 20 drops from 2.43% 2.35% and 2.38%, respectively), demonstrating the necessity of both adaptations for effective alignment. This further shows that dynamically adjusting the alignment strength À (t, d) based on both timestep and preference pair similarity d can help mitigate overfitting to ambiguous preferences (i.e., preference pairs with higher similarity d) and noise (noisy inputs with larger t).
D.2 Generalization Ability of TA-Rec
To answer RQ5, we generalize TCR to multi-step reverse process settings. The results are presented in Figure 5, where the variants "DDPM" and "DDIM" refer to the diffusion-based sequential recommenders based DDPM and DDIM backbones, respectively, while "Ours" denotes that based on TCR training. "Our" consistently outperforms both variants across generation settings of 1-5 steps, as indicated by the pink column's 's superiority over the green and blue columns at various reverse- step settings. Furthermore, the performance of "Ours" remains stable across different reverse-step configurations, validating that TCR generalizes effectively in multi-step generation due to its smooth denoising function.
Additionally, we apply APA to various pre-trained diffusion-based recommenders, with results detailed in Table 7. This includes DDPM-based, DDIM-based, and TCR-based recommenders. For TCR- based recommenders, evaluate outcomes from one-step and multi-step (5-step) generation.
24

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Performance Zhihu
Performance on KuaiRec
Performance Yoochoose
0.90
NDCG@20
NBCG
3.5
2.55
3.0
DDPM DDIK
DOPM
DDPK
3.0
DDIK
DDMM
2.0
Number Reverse Timestop
Number Reverse Timestop
Number Reverse Timestop
Performance of TA-Rec on multi-step inference settings, demonstrating the generalization Figure 5:
ability and robustness of TCR in the multi-step reverse process.
Table 7: Performance of TA-Rec different pretrained diffusion- -obsudd recommenders, demonstrat- ing the generalization ability and effectiveness of APA enhancing diffusion- recommenders' effectiveness.
YooChoose
KuaiRec
Zhihu
Methods
HR@ @20
NDCG@2 20
HR@ @20
NDCG@20
HR @:20
NDCG@20
DDPM
5.68+0.00
3.81+0.03
5.23+0.02
4.23+t.00
2.33#t0.04
0.86+6.03
+APA
5.7210.03
3.8210.01
5.24
4.24
2.3940.02
0.87
DDIM
5.3420.04
3.7520.03
5.05+0.02
3.91
2.38
0.81
+APA
5.3710.03
3.7710.06
5.1420.04
4.07
2.4020.0s
0.8322.03
TCR-1
5.8920.02
3.82+0.05
5.3310.06
4.3020.02
2.41:0.04
0.8940.05
+APA 6.1510.02
4.0610.04
5.40
4.42
2.43.10.04
0.91
TCR-m
5.9310.04
3.88+0.02
5.38+0.03
4.1320.004
2.3722.05
0.86+0.05
+APA
6.00
4.04:10.03
5.40:10.03
4.60.10.04
2.41:10.2
0.8810.04
As shown in Table 7, applying the APA strategy to these pre-trained diffusion-based recommenders yields improved recommendation performance across all three datasets. For instance, "DDIM+APA" enhances the HR @ 20 performance of "DDIM" from 5.05% to 5. KuaiRec. This illustrates the generalization capability of APA, which adaptively aligns denoising models with user preferences to enhance the effectiveness of diffusion-based recommenders.
D.3 Exploration of Different Negative Sampling Strategies of TA-Rec
As presented in Sec 4.4, we randomly sample one item from the item corpus that the user has not interacted with as the negative to construct a preference pair for each Since our main contribution is the adaptive alignment strength, which can mitigate overfitting to ambiguous preferences and noise, the random sampling strategy employed for simplicity.
To explore the impact of different negative sampling strategies, we perform additional experiments on TA-Rec with results presented in Table 8. The "ours-hard" represents selecting hard negatives based on high cosine similarity to the positive item. The "ours-popular" denotes selecting negative items according item popularity. The slightly superior performance of ""ours-popular" and "ours-hard" over "ours" suggests that adopting different negative sampling strategies can be further considered.
D.4 Analysis the Diversity of Diffusion-based Recommenders
For diffusion-baase recommenders, the mechanism adding random noise target items and generating "oracle items" from stochastic noise inherently enhances the diversity of recommendation results. Our model, TA-Res preserves this fundamental mechanism and accelerates generation by smoothing the denoising function. The TCR loss is designed to ensure the discretization enror remains bounded during this accelerated generation. does not eliminate the model's 's dependence on the random noise, so the potential for diverse generation is maintained in TA-Rec.
25

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Table 8: Experiments on different negative sampling strategies.
YooChoose
KuaiRec
Zhihu
Methods
HR@20
NDCG@20
HR@20
NDCG@20
HR @ 20
NDCG@20
w/o APA
5.89
3.82
5.33
4.30
2.41
0.89
ours
6.15
4.06
5,40
4.42
2.43
0.91
ours-popular
6.17
4.07
5.43
4.43
2.43
0.92
ours-hard
6.16
4.08
5.42
4.40
2.45
0.93
Table 9: Experiments on the diversity (coverage@ 20) of diffusion recommenders.
Methods
YooChoose
KuaiRec
Zhihu
SASRec
0.1703
0.8368
0.7306
DreamRec
0.2051
0.8426
0.7616
ours
0,2042
0.8416
0.7609
To validate the diversity of diffusion-based recommender and our TA-Rec, we conduct additional experiments on the coverage metric, which measures the proportion of unique items recommended across multiple users. The experimental results presented in Table 9 indicate that both TA-Rec and DreamRec, which leverage diffusion models for generating recommendations, exhibit greater diversity compared to traditional recommenders like SASRec. This underscores the advantages of diffusion models achieving diverse recommendations. Furthermore, the recommendations from TA-Rec show comparable diversity to those from DreamRec, suggesting that the inherent diversity benefits of diffusion models are maintained even with the faster inference speed.
D.5 Experiments on More Datasets.
Here, we conduct experiments to validate TA-rec on more diverse datasets (Steam [63]. Beauty [64], and Toys), varying in sizes and domains. The statistics of these datasets shown in Table 10. Experimental results are presented in Table R2.
Our method consistently outperforms various baselines larger dataset (Steam) and diverse datasets AAmazon-beurg and Amazon-toys), further highlighting the effectiveness of TA-Rec.
E Discussion and Limitation
E.1
Relationship with Consistency models
Our Temporal Consistency Regularization (TCR) shares conceptual similarities with consistency models [15] in accelerating diffusion-basse based generation by enforcing self-consistency across timesteps. However, TCR differs fundamentally in its design and applicability to sequential recommendation scenarios:
Stochastic vs. Deterministic Trajectories: Consistency models typically enforce consistency along deterministic ODE trajectories, assuming smooth and predefined path for generation. In contrast, TCR operates on the stochastic SDE process [2], which inherently models uncertainty user behavior sequences. This is critical for recommendation systems, where user preferences are noisy and non- deterministic. By regularizing the denoising results of adjacent steps, TCR preserves the stochastic nature of denoising while smoothing the trajectory. making it more robust to the dynamic and noisy patterns of real-world user interactions.
With vs. without distillation: Consistency models often require distilling knowledge from a pre- trained diffusion model, assuming the original model has already captured the data distribution. However, sequential recommendation tasks lack unified pretrained backbone models, making the distillation unreliable. In contrast, TCR is jointly optimized with the item reconstruction loss during pretraining, directly aligning the denoising results with oracle items. This end-to-end approach is independent of numerical solvers, eliminating the need for distillation with complex solvers and ensuring the denoising accuracy under consistency regularization.
26

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Table 10: Statistics the Steam, Beauty, and Toys datasets.
Dataset
Steam
Beauty
Toys
#sequences
281,428
22,363
19,412
#items
13,044
12,101
11,924
#interactions
3,485,022
198,502
167,597
Table The performance of TA-Rec and baseline methods on more datasets(Steam, Beauty, and
Toys).
Steam
Toys
Beauty
Methods
HR @-20
NDCG@ 20
HR TRR@22
NDCG@00
HR @020
NDCG@20
GRU4Ree
10.13
4.21
5.54
3.45
6.46
3.48
Caser
15.12
6.42
8.53
4.21
8.35
4.21
SASRec
13.61
5.36
9.23
4.33
8.98
3.66
Bert4Rec
12.73
5.20
7.49
4.02
8.59
3.45
CL4SRec
15.06
6.12
9.09
5.08
10.18
4.85
DiffRec
15.09
6.89
9.18
5.25
10.21
5.14
DiffuRec
15.83
7.08
10.06
5.18
10.36
5.21
DreamRec
15.08
6.39
5.22
10.32
4.88
DiffuASR
15.74
6.59
9.39
5.19
10.03
5.16
PDRee
15.78
6,51
9.08
10.24
5.02
DimeRec
15.29
6.45
9.15
5.24
10.46
5.44
PreferDiff
15.92
7.12
10.18
5.34
10.69
5.38
Ours 16.25
7.36
10.87
5.81
10,99
5.54
improv.
2.07%
3.37%
6.78%
8.80%
2.81%
1.84%
E.2 Broader Impact
TA-Rec's one-step generation framework substantially reduces the computational overhead of deploy- ing diffusion-based recommender systems industrial settings. By replacing multi-step denoising with single-step process while maintaining accuracy, it enables real-time personalization for latency- critical applications such live- ste-streaming commerce and instant content delivery platforms. These efficiency improvements not only enhance recommendation quality but also boost user engagement and satisfaction real-world applications.
TA-Rec's s approach to accelerating generation while preserving precision provides methodological insights for adapting diffusion models multimodal recommendation tasks (e.g., text, image, and video hybrids), thereby streamlining their adoption in next-generation Al services. This advance- ment bridges the gap between theoretical generative models and scalable, industry-ready solutions, particularly in scenarios requiring seamless integration of heterogeneous data types.
E.3
Limitations and Future Work
Training Cost Limitation: While TA-Rec achieves significant inference efficiency through one- step generation, its two-stage training framework (pretraining with TCR and fine-tuning with APA) requires more computational resources compared to end-to-end approaches. Additionally, the TCR necessitates executing the denoising model twice for the adjacent steps per training iteration, thereby incurring additional computational overhead during the pretraining stage.
Future Work: We will explore integrating large language model (LLM) scaling laws with diffusion- based generative recommendation frameworks to develop diffusion LLM for generative recom- mendation. Specifically, we aim to investigate how LLM emergent capabilities- such as context understanding and semantic reasoning- scale with model size, data volume, and computational resources to enhance the expressive power of diffusion processes in capturing complex user-item interaction patterns.
27


Reviews:
[{"review_id": "lAq4LQiyeV", "reviewer": "Reviewer_BncC", "summary": "This work develop a two-stage diffusion-based recommendation framework to achieve the trade-off between computational efficiency and recommendation effectiveness, which achieve one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning.", "questions": "- If user interaction behavior is sparse, how would this affect the performance of the proposed method? Could it lead to less diverse recommendations?\n- Given the stringent inference time constraints in online systems, how can this method be effectively deployed in production environments?\n- The authors should further discuss the reasonableness of the Lipschitz continuity assumption when applied to real-world data. Does this assumption hold in practical scenarios, and if not, how might it impact the model's performance?\n- Regarding online deployment, does the proposed algorithm support incremental/streaming learning to accommodate real-time recommendation updates based on new user interactions?", "limitations": "See Weaknesses and Questions.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The framework with Temporal Consistency Regularization improves the efficiency without sacrificing the recommendation performance by enforcing the consistency between the denoising results across adjacent steps. \n- TA-Rec introduces Adaptive Preference Alignment module that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps.\n\nWeaknesses\n- Compared with the traditional Recommenders, the developed two-stage diffusion-based recommendation framework need calculate the dot product between x0 and each candidate item in the corpus, and then recommend K items having the highest similarity scores, which requires more computational resources and may limit its application in small-scale scenarios.\n- APA relies on random negative sampling, which may fail to capture hard negative samples, thereby affecting the effectiveness of preference alignment.\n- This work does not thoroughly explore the method's performance in data-sparse or cold-start scenarios.", "was_revised": true, "final_justification": "I have read the author rebuttal and considered all raised points., I have engaged in discussions and responded to authors. While the rebuttals addressed my main concerns, I believe a Borderline Accept score remains fair for this paper. I will therefore maintain my original score.", "rebuttal": "Thanks for your constructive suggestions. We have detailed our responses **point-by-point** to address your concerns. \n\n### Weakness 1: \n> The computational resources for the dot product between $x_0$ and candidate items\n\n- **Calculating similarity between $x_0$ and item corpus then recommending the top-K is a standard final step in recommendation systems (e.g., SASRec).** \nIt is not unique to our framework. Crucially, the computational cost of dot products is negligible compared to model inference as it involves only simple matrix multiplication after embeddings are generated.\n\n- **Our key innovation lies in accelerating the generation of embedding ($x_0$)**. As shown in Table 3, TA-Rec's inference time is lower than other diffusion recommenders and comparable to traditional recommenders like SASRec. \n\n\n\n\n### Weakness 2\n> Random negative sampling of APA may fail to capture hard negative samples for preference alignment.\n\n\n- The negative sampling strategy and our primary innovation of the adaptive alignment coefficient $\\lambda_\\beta(t,d)$ are **independent effective solutions**. The coefficient $\\lambda_\\beta(t,d)$ dynamically adjusts the optimization strength to mitigate overfitting to ambiguous preferences and noise. **The effectiveness of APA is validated in the Sec 5.3 of our paper**. The random negative sampling we employed is indeed a simplification, and **we have acknowledged it as a limitation in Appendix E.3 of our paper**.\n\n\n- We consider your suggestion a valuable direction for future research, and we conduct additional experiments to explore hard negative sampling strategy in Table R1. The ``w/ hard`` samples 5 random negatives, selects the most similar to the target via cosine similarity as a hard negative, and conducts preference alignment without adaptive coefficients. ``ours-hard`` incorporates APA with hard negative sampling for adaptive alignment. The superiority of ``w/ hard`` over ``w/o APA`` and ``ours-hard`` over ``ours`` **demonstrates the potential of hard negative sampling**.\n\n**Table R1:**  Experiments on hard negative sampling strategy.\n| Methods | YooChoose |  | KuaiRec |  | Zhihu |  |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n|  | H@20| N@20|H@20| N@20| H@20 | N@20|\n|w/o APA| 5.89|3.82| 5.33|4.30|2.41|0.89|\n|w/ hard|6.08|4.01|5.36|4.39|2.40|0.92|\n|ours|6.15|4.06|5.40|4.42|2.43|0.91|\n|ours-hard|6.16|4.08|5.42|4.40|2.45|0.93|\n\n\n\n\n### Weakness 3\n> Performance in data-sparse or diverse scenarios\n\n- While cold-start and diversity are important challenges in recommendation systems, they are orthogonal to our core contribution: ​​resolving the fundamental efficiency-effectiveness trade-off in diffusion-based recommenders​​ deployment. \n\n- Extending our approach to more challenging scenarios, such as cold-start problems or diversity, could be a focus for future research. Here, we construct datasets in data-sparse setting for exploration, by removing 30% interactions. The experimental results are presented in Table R2. **The superior performance of our method compared to the baseline indicates its robustness in data-sparse scenarios**. Additionally, to validate the diversity of our TA-Rec, we conduct additional experiments on the coverage metrics, which measure the proportion of unique items recommended across multiple users. The experimental results presented in Table R3 indicate that both TA-Rec and DreamRec, which leverage diffusion models for generating recommendations, exhibit greater diversity compared to traditional recommenders like SASRec. **This underscores the advantages of TA-Rec in achieving diverse recommendations**. \n\n\n**Table R2:**  Experiments on data-sparse scenarios.\n| Methods | YooChoose |  | KuaiRec |  | Zhihu |  |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n|  | H@20 | N@20| H@20 | N@20| H@20| N@20|\n|SASRec|2.89|1.45|3.28| 1.19|1.44|0.57|\n|DreamRec|4.29|2.15|5.02|3.96|2.15|0.77|\n|ours|5.65|3.46|5.19|4.25|2.36|0.89|\n\n\n**Table R3:**  Experiments on the diversity of diffusion recommenders.\n| Methods | YooChoose  | KuaiRec |   Zhihu |  \n| :---: | :---: | :---: | :---: | \n|  | Cover@20| Cover@20| Cover@20|\n|SASRec|0.1703|0.8368|0.7306|\n|DreamRec|0.2051|0.8426|0.7616|\n|ours|0.2042|0.8416|0.7609|\n\n\n\n### Questions:\n> Online Deployment and Inference Time & Lipschitz Continuity Assumptions & Online Learning\n\n(1) By achieving one-step generation with TCR in the pretraining stage, TA-Rec has similar inference time to traditional recommenders (e.g., SASRec), as shown in Table 3 of our paper. **Thus, our method can be effectively deployed in production environments**. (2) The Lipschitz continuity assumption is not an inherent assumption applied to data, **but rather a desirable property that we realize on the denoising function $f_\\theta$ through TCR objective**. By constraining TCR loss, we enforce local smoothness between adjacent denoising steps and ensure $f_\\theta$ satisfies the Lipschitz continuity assumption. Then, $f_\\theta$ can achieve one-step generation with bounded error, mapping noise directly to oracle items as shown in Theorem 1. \n(3) Our algorithm primarily focuses on offline training setting, which is consistent with comparable methods like DreamRec and PreferDiff. We agree that it can support real-time incremental and streaming learning, which is a potential direction for future work. \n\n\nWe sincerely hope that our response has adequately **addressed your concerns**. If so, we would greatly appreciate your consideration in **raising the score**. If there are any remaining concerns, **please let us know**, and we will **continue to actively address your comments** and **improve our work**."}, {"review_id": "An48AXiS3F", "reviewer": "Reviewer_NQjp", "summary": "This paper introduces a two-stage diffusion framework that achieves one-step generation via Temporal Consistency Regularization (TCR) during pre-training and reduces trajectory deviation through Adaptive Preference Alignment (APA) in fine-tuning. Extensive experiments demonstrate the method’s effectiveness in mitigating discretization-error-induced trade-offs, supported by theoretical analysis proving bounded error for the proposed TA-Rec model.", "questions": "1. Could you provide heuristic guidance for selecting important hyper-parameters, such as λ_β_base?\n2. What specific negative sampling strategy  is used in APA?\nHave alternative strategies been explored? If so, how do they influence performance ?\n3. APA shows limited improvement over other diffusion recommenders based on table 7. Are there any further discussion and analysis?\n​\n \n​", "limitations": "1. ​​Dataset diversity insufficiency​​, limiting claims of broad applicability.\n​​2. Omitted computational complexity analysis​​, hindering practical adoption assessment.", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Pros:\n\n1. The integration of TCR and APA offers a novel approach to balancing generation efficiency and trajectory alignment, addressing a significant challenge in diffusion-based recommenders.\n2. The bounded-error analysis provides a theretical foundation for the method’s rationality.\n3. Experiments from multiple angles (e.g., ablation studies, comparison with baselines) convincingly validate the design choices.\n4. The paper is well-structured, with clear exposition of motivations, methodology, and results.\n\nCons:\n\n1. Experiments are conducted on only ​​three datasets​​ of similar scales. To ensure generalizability, validation on more diverse datasets  is essential .\n2. The theretical computational costs (time/memory) of TA-Rec, especially during training, are ​​not quantified​​. As scalability is critical for real-world deployment, asymptotic complexity analysis .\n3. The negative sampling strategy for APA lacks ​​implementation details​​ and ​​sensitivity analysis​​. Its impact on preference alignment efficacy requires rigorous ablation.", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your constructive suggestions. We have detailed our responses **point-by-point** to address your concerns. \n\n### Weakness 1\n> Experiments on more diverse datasets.\n \nHere, we **conduct experiments** to validate TA-rec on more diverse datasets (Steam, Beauty, and Toys), varying in sizes and domains. The statistics of these datasets are shown in Table R1. Experimental results are presented in Table R2. \n\n**Table R1:**  Statistics of the Steam, Beauty, and Toys datasets.\n|             |   Sequence  |     Items    |Ineteractions|\n| ----------- | ----------- | ------------ |------------ |\n|    Steam    |   281,428   |    13,044    | 3,485,022   |\n|   Beauty    |   22,363    |    12,101    |   198,502   |\n|    Toys     |   19,412    |    11,924    |   167,597   |\n\n**Table R2:** Overall performance of different methods for the sequential recommendation on diverse datasets. \n|Methods|Steam||Toys||Beauty||\n|:------------|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|\n|  | H@20| N@20 | H@20| N@20 | H@20| N@20 |\n|GRU4Rec|10.13|4.21|5.54|3.45|6.46|3.48|\n|Caser|15.12|6.42|8.53|4.21|8.35|4.21|\n|SASRec|13.61|5.36|9.23|4.33|8.98|3.66|\n|Bert4Rec|12.73|5.20|7.49|4.02|8.59|3.45|\n|CL4SRec|15.06|6.12|9.09|5.08|10.18|4.85|\n|DiffRec|15.09|6.89|9.18|5.25|10.21|5.14|\n|DiffuRec|15.83|7.08|10.06|5.18|10.36|5.21|\n|DreamRec|15.08|6.39|9.88|5.22|10.32|4.88|\n|DiffuASR|15.74|6.59|9.39|5.19|10.03|5.16|\n|PDRec|15.78|6.51|9.08|5.12|10.24|5.02|\n|DimeRec|15.29|6.45|9.15|5.24|10.46|5.44|\n|PreferDiff|15.92|7.12|10.18|5.34|10.69|5.38|\n|Ours|16.25|7.36|10.87|5.81|10.99|5.54|\n|improv.|2.07%|3.37%|6.78%|8.80%|2.81%|1.84%|\n\nOur method consistently outperforms various baselines on **larger dataset** (Steam) and **diverse datasets** (Amazon-beauty and Amazon-toys), **further highlighting the effectiveness of TA-Rec**. \n\n\n### Weakness 2\n> Theoretical computational costs of TA-Rec.\n\nLet $N$ be the batch size, $L$ be the sequence length, and $D$ be the embedding dimension. The primary components of our model are the Transformer encoder and the MLP-based denoising model.\n\n**Inference Complexity**\n\nThe primary contribution of TA-Rec is the significant reduction in inference complexity compared to prior diffusion models.\n\n* **Traditional Recommender (with Transformer and MLP in model design):**  The complexity of self-attention in Transformer is $O(N \\cdot L^2 \\cdot D )$, and the complexity of MLP is $O(N \\cdot D^2)$, so the total inference complexity is $O(N \\cdot L^2 \\cdot D + N \\cdot D^2)$. \n\n\n* **Previous Diffusion Recommender (e.g., DreamRec):** The complexity is dependent on the number of denoising steps ($T$), which is typically large (~1000).\n    * **Complexity Formula:** $O(T\\cdot(N \\cdot L^2 \\cdot D +  \\cdot N \\cdot D^2))$\n* **TA-Rec:** By enabling one-step generation ($T=1$), the inference complexity is no longer dependent on a large number of steps and becomes comparable to traditional Transformer-based models.\n    * **Complexity Formula:** $O(N \\cdot L^2 \\cdot D + N \\cdot D^2)$\n\nThus, the inference complexity of TA-Rec is comparable to traditional transformer-based recommenders (e.g., SASRec).\n\n**Training Complexity:**\n* **Traditional Recommender (e.g., SASRec):** Due to Transformer and MLP modules, the training complexity is $O(N \\cdot L^2 \\cdot D + N \\cdot D^2)$. \n\n* **Previous Diffusion Recommender (e.g., DreamRec):** The reconstruction loss $\\mathcal{L}_{diff}$ requires guidance encoding from the Transformer and a denoising process through the MLP denoising model. The training complexity is $O( N \\cdot L^2 \\cdot D + N \\cdot D^2)$.\n* **TA-Rec:** \nThe ​​TCR loss​​ adds one extra MLP forward pass for an adjacent timestep. The training complexity of TA-Rec is $O(N \\cdot L^2 \\cdot D +2* N \\cdot D^2)$, which is comparable to traditional recommender since the complexity of MLP is negligible vs. Transformer.\n\n**The time cost comparison in Table 3 of our paper validates the efficiency of TA-Rec for real-world deployment**.\n\n\n\n### Weakness 3 & Question 2\n> Implementation details and ​sensitivity analysis​ for the negative sampling strategy for APA. \n\n- As presented in Sec 4.4 of our paper, we randomly sample one item from the item corpus that the user has not interacted with as the negative to construct a preference pair for each user. Since our main contribution is the **adaptive alignment strength** which can mitigate overfitting to ambiguous preferences and noise, the random sampling strategy is employed for simplicity. \n\n- To conduct ​sensitivity analysis on the negative sampling strategy, we perform additional experiments to explore different negative sampling strategies in Table R3. The ``ours-hard`` represents selecting hard negatives based on high cosine similarity to the positive item. The ``ours-popular`` denotes selecting negative items according to item popularity. The slightly superior performance of ``ours-popular`` and ``ours-hard`` over ``ours`` suggests that adopting different negative sampling strategies can be further considered.\n\n**Table R3:**  Experiments on hard negative sampling strategy.\n| Methods | YooChoose |  | KuaiRec |  | Zhihu |  |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n|  | H@20| N@20| H@20| N@20| H@20| N@20|\n|w/o APA| 5.89|3.82| 5.33|4.30|2.41|0.89|\n|ours|6.15|4.06|5.40|4.42|2.43|0.91|\n|ours-popular|6.17|4.07|5.43|4.43|2.43|0.92|\n|ours-hard|6.16|4.08|5.42|4.40|2.45|0.93|\n\n\n\n\n### Questions\n> Heuristic guidance for selecting $\\lambda_{base}$.\n\n- In DPO loss, the hyperparameter $\\lambda_\\beta$ typically ranges from 0.01 to 0.9. As shown in Figure 3 of our paper, the alignment strength $\\lambda_\\beta=1/2$ can achieve optimal performance across diverse datasets. As a result, we set $\\lambda_{base}=1/2$ and vary $\\lambda_\\beta(t,d)$ from 0 to 1 with the adaptive mechanism.\n\n> Limited improvement of APA over other diffusion recommenders in Table 7.\n- After further calculations and validations, the performance improvement of APA over other diffusion recommenders ranges from 1% to 4%. All results are the average of 5 independent runs, and these improvements are statistically significant (p-value < 0.05). The improvements observed in TCR and DDIM exceed those in DDPM, indicating that APA may be more effective in enhancing the accuracy of methods designed to accelerate generation.\n\n\nWe sincerely hope that our response has adequately **addressed your concerns**. If so, we would greatly appreciate your consideration in **raising the score**. If there are any remaining concerns, **please let us know**, and we will **continue to actively address your comments** and **improve our work**."}, {"review_id": "AZQblnkiaZ", "reviewer": "Reviewer_J9pE", "summary": "This paper addresses diffusion-based sequential recommendation systems. Unlike existing models that require many steps (e.g., 1,000 steps) to improve accuracy, this work reduces the process to a single step to achieve efficiency. The model consists of a pretraining phase called the temporal consistency regularizer (TCR) and a finetuning phase called adaptive preference alignment (APA), where the system is further trained to better match user preferences. Compared to prior diffusion models, it is significantly faster while also improving accuracy.", "questions": "1. I think there is a possibility that the advantage of diversity inherent in diffusion models has been reduced in exchange for faster inference speed. Why was no analysis or experiment on this aspect included in the paper?\n2. As mentioned earlier, if TCR removes the differences between steps to enable 1-step generation, I am curious whether the diffusion process is really functioning as intended. In the ablation study, it is shown that the performance of w/o APA is higher than DDPM and DDIM, which involve multi-step operations. It is puzzling how a single-step generation can outperform traditional multi-step diffusion methods.", "limitations": "While the authors have thoroughly evaluated the effectiveness and efficiency of their proposed approach, there is no discussion of potential negative societal impacts.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "- **Quality**: The paper’s contributions are clear, and the theoretical analysis appears to be well-grounded. However, it seems to lack an analysis of the diversity typically associated with diffusion-based models, which could be an area for improvement.\n- **Clarity**: The structure and flow of the paper are clear, and the figures and tables are well-organized.\n- **Significance**: By substantially improving the efficiency of diffusion-based models—whose main drawback is slow inference—the work makes a meaningful contribution. However, if TCR essentially removes inter-step differences to enable 1-step generation, it raises the question of whether using a diffusion model is necessary at all. The core of diffusion is to generate the desired distribution through denoising noise; if the noise and the output are almost indistinguishable, it is questionable whether diffusion is truly being applied effectively.\n- **Originality**: The finetuning process using adaptive preference alignment is well-tailored to recommendation systems, and the resulting performance improvements are notable.", "was_revised": true, "final_justification": "My concerns about diversity loss and the necessity of diffusion in 1-step generation were addressed through additional coverage@20 experiments and clear theoretical explanations. The paper is technically solid, with strong experimental validation and a well-structured presentation. TCR and APA are effectively adapted for the recommender system context, and the results demonstrate both efficiency and effectiveness gains.", "rebuttal": "Thanks for your constructive suggestions. We have detailed our responses **point-by-point** to address your concerns. \n\n\n### Weakness 1 and Question 1:\n> Analysis of the diversity associated with diffusion-based models\n\nFor diffusion models, the mechanism of **adding random noise** to target items and **generating \"oracle items\" from stochastic noise** inherently enhances the diversity of recommendation results.\nOur model, TA-Rec, preserves this fundamental mechanism and accelerates generation by smoothing the denoising function. The TCR loss is designed to ensure the discretization error remains bounded during this accelerated generation. It does not eliminate the model's dependence on the random noise,\nso the potential for diverse generation is maintained in TA-Rec.\n\nTo validate the diversity of diffusion-based recommender and our TA-Rec, we conduct additional experiments on the coverage metric, which measures the proportion of unique items recommended across multiple users\nThe experimental results presented in Table R1 indicate that both TA-Rec and DreamRec, which leverage diffusion models for generating recommendations, exhibit greater diversity compared to traditional recommenders like SASRec. **This underscores the advantages of diffusion models in achieving diverse recommendations**. Furthermore, the recommendations from TA-Rec show comparable diversity to those from DreamRec, suggesting that **the inherent diversity benefits of diffusion models are maintained even with the faster inference speed**.\n\n**Table R1:**  Experiments on the diversity of diffusion recommenders.\n| Methods | YooChoose  | KuaiRec |   Zhihu |  \n| :---: | :---: | :---: | :---: | \n|  | Cover@20| Cover@20| Cover@20|\n|SASRec|0.1703|0.8368|0.7306|\n|DreamRec|0.2051|0.8426|0.7616|\n|ours|0.2042|0.8416|0.7609|\n\n\n\n### Weakness 2 and Question 2:\n> Removing inter-step differences to enable 1-step generation raises the question of whether using a diffusion model is necessary. \n\n\n(1) **Diffusion is a superior training mechanism**: The core mechanism of diffusion models involves **adding random noise to items** and then training a model to **generate \"oracle items\" from stochastic noise**. We consider this a powerful training mechanism that enriches item distribution, facilitates the learning of item representations, and generates high-quality items to recommend. \nWhile effective, the multi-step reverse process often leads to significant inference overhead.\nOur proposed model, TA-Rec, accelerates the reverse process to a single denoising step with TCR while **adhering to the fundamental diffusion mechanism**. The value of the diffusion mechanism is empirically demonstrated by comparing TA-Rec to a strong baseline like SASRec (directly predicts next items without diffusion processing). As shown in Table 1 of our paper, TA-Rec significantly outperforms SASRec, even though both utilize similar core modules (e.g., Transformer, MLP). This performance gap **highlights the necessity of the diffusion process for learning higher-quality representations and generating oracle items**. \n\n(2) **Whether the noise and generation are indistinguishable in TA-Rec**: The one-step generation of TA-Rec is achieved by smoothing the denoising function $f_\\theta$ via Temporal Consistency Regularization (TCR), which enforces $f_\\theta(x_t,t,g)\\approx f_\\theta(x_{t-1},t-1,g)$ during the noise-to-output mapping. The TCR does not eliminate inter-step differences between $x_t$, $x_{t-1}$, and $x_0$, but strategically smooths the $f_\\theta$ to **ensure consistent denoising output $x_0$ across different steps $t$**. This aligns with the concepts of \"Consistency Models\".\n\n\n(3) **Why our one-step generation outperforms multi-step samplers DDPM/DDIM**: DDPM/DDIM approximate a continuous reverse-SDE process with **finite steps** $T$ (typically 1000). This introduces the **truncation errors that increase as T decreases** (as discussed in Definition 1 of our paper). Even with $T=1000$, DDPM/DDIM converges to a ​​lossy approximation​​ of the true data distribution $P(x_0)$. For TA-Rec, the Temporal Consistency Regularization (TCR) enforces $f_\\theta(x_t,t,g) - f_\\theta(x_{t-1},t-1,g)\\leq \\delta$ to smooth the denoising trajectory. By maintaining output consistency across all adjacent steps, TCR ensures that $f_\\theta(x_T, T,g)\\approx f_\\theta(x_t,t,g) \\approx x_0 $. Thus, ``w/ APA`` with one-step generation ​​implicitly integrates the infinite-step SDE (infinite $T$)​​, alleviating truncation errors and achieving better performance. \n\n\n\n### Limitation:\n> Discussion of potential negative societal impacts.\n\nOver-personalization can lead to filter bubbles or echo chambers. Immediate feedback mechanisms may encourage users to engage compulsively, which could negatively impact their mental health.\n\n\n\nWe sincerely hope that our response has adequately **addressed your concerns**. If so, we would greatly appreciate your consideration in **raising the score**. If there are any remaining concerns, **please let us know**, and we will **continue to actively address your comments** and **improve our work**."}, {"review_id": "sYBZurdMnr", "reviewer": "Reviewer_dntH", "summary": "This paper proposes a two-stage framework called TA-Rec to address the trade-off between efficiency and effectiveness in diffusion-based sequential recommendation models. The framework includes Temporal Consistency Regularization (TCR) in the pretraining stage to smooth the denoising function and enable one-step generation, while introducing Adaptive Preference Alignment (APA) in the fine-tuning stage to align the denoising process more closely with user preferences. Experimental results show that TA-Rec achieves 100× faster generation compared to leading diffusion-based recommendation methods while improving recommendation performance by 10%. Theoretical analysis demonstrates that TA-Rec can bound the error of accelerated generation, thereby enhancing the reliability of recommendations.", "questions": "1. The experimental dataset may lack diversity, more experimental dataset would be prefered.", "limitations": "The TCR module necessitates executing the denoising model twice per training iteration for adjacent steps, increasing computational overhead during the pretraining stage.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. APA dynamically adjusts the alignment strength based on preference pair similarity and timesteps during fine-tuning, enhancing recommendation accuracy. This framework effectively addresses the efficiency-effectiveness trade-off in diffusion-based recommendation models.\n2. The paper demonstrates that TA-Rec achieves 100× faster generation compared to leading diffusion-based recommenders (e.g., DreamRec) while improving recommendation performance by 10%. \n3. The paper provides theoretical analysis, proving that TA-Rec's one-step generation has bounded error and that its effectiveness aligns with user preferences. This theoretical foundation reinforces the credibility of the framework and its applicability.\n4. This paper is well-written and easy to follow. \n5. This paper is well-motivated and verified. \n\nWeakness:\n1. the TCR module necessitates executing the denoising model twice per training iteration for adjacent steps, increasing computational overhead during the pretraining stage.\n2. The experimental dataset may lack diversity, more experimental dataset would be prefered.", "was_revised": true, "final_justification": "This paper proposes a two-stage framework called TA-Rec to address the trade-off between efficiency and effectiveness in diffusion-based sequential recommendation models. The framework includes Temporal Consistency Regularization (TCR) in the pretraining stage to smooth the denoising function and enable one-step generation, while introducing Adaptive Preference Alignment (APA) in the fine-tuning stage to align the denoising process more closely with user preferences. Experimental results show that TA-Rec achieves 100× faster generation compared to leading diffusion-based recommendation methods while improving recommendation performance by 10%. Theoretical analysis demonstrates that TA-Rec can bound the error of accelerated generation, thereby enhancing the reliability of recommendations.\n\nAPA dynamically adjusts the alignment strength based on preference pair similarity and timesteps during fine-tuning, enhancing recommendation accuracy. This framework effectively addresses the efficiency-effectiveness trade-off in diffusion-based recommendation models. The paper demonstrates that TA-Rec achieves 100× faster generation compared to leading diffusion-based recommenders (e.g., DreamRec) while improving recommendation performance by 10%.  The paper provides theoretical analysis, proving that TA-Rec's one-step generation has bounded error and that its effectiveness aligns with user preferences. This theoretical foundation reinforces the credibility of the framework and its applicability. This paper is well-written and easy to follow. This paper is well-motivated and verified.", "rebuttal": "Thanks for your constructive suggestions. We have detailed our responses **point-by-point** to address your concerns. \n\n\n### Weakness 1\n> Computational cost of executing the denoising model twice per training iteration for adjacent steps due to TCR loss.\n\n#### Training Complexity\n* **Previous Diffusion Recommender (e.g., DreamRec):** The reconstruction loss $\\mathcal{L}_{diff}$ requires guidance encoding from the Transformer and a denoising process through the MLP denoising model. The training complexity is $O (N \\cdot L^2 \\cdot D + N \\cdot D^2)$.\n* **TA-Rec:** \nThe ​​TCR loss​​ adds one extra MLP forward pass for an adjacent timestep. The training complexity of TA-Rec is $O(N \\cdot L^2 \\cdot D +2* N \\cdot D^2)$, which is comparable to previous diffusion recommender since the complexity of MLP is negligible vs. Transformer.\n\n**Table 3 in our paper provides a comparative analysis of training time costs, demonstrating the efficiency of TA-Rec**. Importantly, the integration of TCR loss during training facilitates **one-step generation during inference**, thereby significantly enhancing the efficiency of diffusion-based generative recommendation in real-world deployment.\n\n\n\n### Weakness 2\n> Experiments on more diverse datasets.\n\n Here, we **conduct experiments** to validate TA-rec on more diverse datasets (Steam, Beauty, and Toys), varying in sizes and domains. The statistics of these datasets are shown in Table R1. Experimental results are presented in **Table R2**. \n\n**Table R1:**  Statistics of the Steam, Beauty, and Toys datasets.\n|             |   Sequence  |     Items    |Ineteractions|\n| ----------- | ----------- | ------------ |------------ |\n|    Steam    |   281,428   |    13,044    | 3,485,022   |\n|   Beauty    |   22,363    |    12,101    |   198,502   |\n|    Toys     |   19,412    |    11,924    |   167,597   |\n\n**Table R2:** Overall performance of different methods for the sequential recommendation on diverse datasets. \n|Methods|Steam||Toys||Beauty||\n|:------------|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|\n|  | H@20| N@20 | H@20| N@20 | H@20| N@20 |\n|GRU4Rec|10.13|4.21|5.54|3.45|6.46|3.48|\n|Caser|15.12|6.42|8.53|4.21|8.35|4.21|\n|SASRec|13.61|5.36|9.23|4.33|8.98|3.66|\n|Bert4Rec|12.73|5.20|7.49|4.02|8.59|3.45|\n|CL4SRec|15.06|6.12|9.09|5.08|10.18|4.85|\n|DiffRec|15.09|6.89|9.18|5.25|10.21|5.14|\n|DiffuRec|15.83|7.08|10.06|5.18|10.36|5.21|\n|DreamRec|15.08|6.39|9.88|5.22|10.32|4.88|\n|DiffuASR|15.74|6.59|9.39|5.19|10.03|5.16|\n|PDRec|15.78|6.51|9.08|5.12|10.24|5.02|\n|DimeRec|15.29|6.45|9.15|5.24|10.46|5.44|\n|PreferDiff|15.92|7.12|10.18|5.34|10.69|5.38|\n|Ours|16.25|7.36|10.87|5.81|10.99|5.54|\n|improv.|2.07%|3.37%|6.78%|8.80%|2.81%|1.84%|\n\nOur method consistently outperforms various baselines on **larger dataset** (Steam) and **diverse datasets** (Amazon-beauty and Amazon-toys), **further highlighting the effectiveness of TA-Rec**. \n\n\nWe sincerely hope that our response has adequately **addressed your concerns**. If so, we would greatly appreciate your consideration in **raising the score**. If there are any remaining concerns, **please let us know**, and we will **continue to actively address your comments** and **improve our work**."}, {"review_id": "wz5hXZuDjh", "reviewer": "Reviewer_ptjv", "summary": "This paper introduces TA-Rec, a novel two-stage framework addressing the critical efficiency-effectiveness trade-off in diffusion-based sequential recommenders. The pretraining stage involves Temporal Consistency Regularization (TCR) to enforce consistency among denoising results, enabling efficient one-step generation of the next item (oracle item). Subsequently, the model undergoes fine-tuning with Adaptive Preference Alignment (APA) to align the denoising process more effectively with user preferences. The authors demonstrate the superiority of their proposed framework in both computational efficiency (inference time) and recommendation effectiveness (performance).", "questions": "1. In Table 1, the number of reverse steps is stated only for DreamRec and PreferDiff. Could you clarify the number of reverse steps used by other methods (e.g., DiffRec, DiffuRec) included in the comparison?\n\n2. In Table 3, does the reported training time for TA-Rec encompass the cumulative duration of both the pretraining stage and fine-tuning stage?\n\n3. The paper mentions that the Transformer model is frozen during the fine-tuning stage. Could you explain this design choice? Is there any experimental analysis? Were there any specific reasons that lead to this decision? (e.g., stability, cost)\n\n4. The use of a one-step reverse process, even with guidance embedding, might inherently lead to a decrease in the diversity of generated items or a tendency towards uniformized item embeddings. Could you suggest or conduct additional analyses to asses the diversity of the recommended items or the characteristics of the generated embedding space or item embedding space? (e.g., embedding space visualization techniques like t-SNE)\n\n5. There are some typos and errors.\na. In Table 1, in the case of dataset \"YooChoose\", the second-best baseline is DimeRec, not PDRec.\nb. In Table 3, in the case of dataset \"YooChoose\", there is typo \"traning\", not \"training\".", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 3, "originality": 1, "strengths_and_weaknesses": "## Strengths\n\n1. The paper is well-written and well-structured, enhancing its readability and clarity.\n\n2. This work represents a pioneering effort by adopting one-step diffusion within a sequential recommender context. The authors validated the one-step diffusion approach through extensive experiments, demonstrating significant improvements in both speed and accuracy. This work can effectively resolve a core challenge (inference time) in diffusion-based recommenders.\n\n3. The paper proposes a recommender-specific Temporal Consistency Regularization (TCR) to smooth the denoising steps. This crucial component directly enables the feasibility and effectiveness of the one-step diffusion process.\n\n4. The paper provides a mathematical proof for the effectiveness of Adaptive Preference Alignment (APA), an approach developed upon the concept of Direct Preference Optimization (DPO).\n\n## Weaknesses\n\n1. The novelty of this paper is questionable. Given that the Temporal Consistency Regularization (TCR) is derived from the concept of \"Consistency Model\" and Adaptive Preference Alignment (APA) derived from \"Direct Preference Optimization (DPO)\", the proposed methodology could be perceived as a refinement of existing concepts rather than a fundamentally new contribution.\n\n2. It would be beneficial to include experiments analyzing the impact of the scale of $\\lambda_{base}$. Specifically, demonstrating how performance changes with varying $\\lambda_{base}$ value without the time step and similarity terms would provide deeper insights.\n\n3. The premise of Theorem 1, which assumes the pretraining loss as zero, is highly unrealistic. This assumption fundamentally undermines the reliability and practical applicability of the theorem's conclusion, as such an ideal state is unattainable, especially in a real-world recommender scenario.", "was_revised": true, "final_justification": "I thank the authors for their detailed rebuttal and the insightful discussion. While i acknowledge the paper's strengths and strong empirical results, my assessment regarding its limited technical novelty has not changed. In my view, the work represents a high-quality engineering effort but lacks a sufficient conceptual advance to warrant a higher rating. Consequently, I will be keeping my score unchanged.", "rebuttal": "Thanks for your constructive suggestions. We have detailed our responses **point-by-point** to address your concerns. \n\n\n### Weakness 1:\n> The novelty of this paper is questionable. \n\nThanks for your question. However, we respectfully argue that our primary contribution is not the invention of these base concepts, but rather the **novel task, technologies designed for recommenders, and theoretical analysis**.\n\n**For tasks**: \nWe are the first to identify and address the specific efficiency-effectiveness trade-off in diffusion-based recommenders. This problem arises from the discretization errors inherent in the multi-step generation process.\n\n**For technologies**: Our methods are not direct applications of existing methods but are novelly adapted for the recommendation context.\n- **Novelty in TCR for Recommendation**: Unlike standard Consistency Models which operate on deterministic ODEs, our TCR is designed for the stochastic SDE/DDPM framework, which is crucial for the stochastic nature of user behavior. \nFurthermore, TCR loss is trained jointly with the diffusion reconstruction loss $\\mathcal{L}_{diff}$, circumventing the need for a trained teacher diffusion model, which is often impractical in the recommendation domain.\n\n- **Novelty in APA for Diffusion Alignment**: Similarly, our APA is more than a simple application of DPO in diffusion models. Its novelty lies in the introduction of the **adaptive coefficient**, $\\lambda_\\beta(t,d)$, which dynamically modulates the optimization strength based on both the timestep (noise level) and preference pair similarity. This can avoid diffusion recommenders' overfitting to ambiguous preferences or high noise levels.\n\n**For theoretical analysis**: Our theoretical analysis provides a rigorous foundation for our framework. This includes the analysis of the bounded error of our one-step generation (Theorem 1) and the impact of our adaptive alignment coefficient (Theorem 2), distinguishing our work from a simple application of existing concepts.\n\n\n### Weakness 2:\n> Analyzing the impact of the scale of $\\lambda_{base}$ without the time step and similarity terms.\n\nThanks for your comment. We have analyzed the sensitivity of TA-Rec to the $\\lambda_\\beta$ ($\\lambda_{base}$) without the time step and similarity terms in Figure 4 of our paper. The methods of ``fix_one`` and ``fix_mul`` denote fine-tuning TA-Rec with\nfixed $\\lambda_\\beta$ without adapting to timestep or similarity in one or multiple steps-generation. The curves of ``fix-one`` and ``fix-mul`` fluctuate as $\\lambda_\\beta$ ($\\lambda_{base}$) changes, highlighting the significance of $\\lambda_{\\beta}$ in performance optimization. The curves of ``Ours_one`` and ``Ours_mul``, which consider timestep and similarity terms, lie above those of ``fix_one`` and ``fix_mul``, validating the superiority of our design of adaptive coefficient $\\lambda_\\beta(t,d)$ in aligning user preference. \n\n### Weakness 3:\n> The premise of Theorem 1, which assumes the pretraining loss as zero, is highly unrealistic. \n\nThanks for your comment. We agree that the premise of zero pretraining loss in the original Theorem 1 is an idealization for the sake of clarity, and is unattainable in practice. To address this and enhance the theorem's practical applicability, we have reformulated the error bound to depend on the non-zero pretraining loss:\n\n$$\\begin{aligned}\n       ||f_\\theta(\\mathbf{x}\\_{s/\\Delta t}, \\mathbf{g}, s/\\Delta t) - \\mathbf{x}||\\_2 &\\le ||f\\_\\theta(\\mathbf{x}\\_{s/\\Delta t}, \\mathbf{g}, s/\\Delta t) - f\\_\\theta(\\mathbf{x}\\_{t},\\mathbf{g},{t})||\\_2 + ||f\\_\\theta(\\mathbf{x}\\_{t},\\mathbf{g},{t}) - \\mathbf{x}||\\_2\\\\\\\\\n      & \\leq ||f\\_\\theta(\\mathbf{x}\\_{s/\\Delta t}, \\mathbf{g}, s/\\Delta t) - f\\_\\theta(\\mathbf{x}\\_{t},\\mathbf{g},{t})||\\_2+\\delta.\n    \\end{aligned}\n$$\n\nThe second term $||f\\_\\theta(\\mathbf{x}\\_{t},\\mathbf{g},{t}) - \\mathbf{x}||\\_2$ represents denoising model's reconstruction error. This error is inherent to denoising model, regardless of whether a multi-step or our accelerated one-step inference is used. Instead of assuming it is zero, we now bound it by the pretraining loss $\\mathcal{L}\\_{pre}$. We can have $||f\\_\\theta(\\mathbf{x}\\_{t}, \\mathbf{g}, t) - \\mathbf{x}||\\_2<\\delta$. Since we minimize the $L\\_{pre}$ loss, the error of one-step generation is still bounded.\n\n### Questions:\n> 1. The number of reverse steps used by other methods (e.g., DiffRec, DiffuRec) in Table 1?\n\nThe reverse steps for DiffuRec and DiffRec are chosen in [10,100,500,1000]. They are configured as [100,500,200] for DiffuRec and [100,500,100] for DiffRec, respectively, in the Yoochoose, KuaiRec, and Zhihu datasets.\n\n\n> 2. The reported training time for TA-Rec \n\nThe reported training times in Table 3 reflect ​the time cost​​ for each epoch in the independent phase (pretraining/fine-tuning), not the cumulative time for the two stages.\nThe per-epoch training time for each stage is comparable to baselines (e.g., SASRec), as the main computational cost in these methods comes from the Transformer encoder. \nThe extra computation required in the pre-training stage is negligible, as it only involves a second pass through the lightweight MLP denoising model.\n\n\n> 3. Explanation of the design of the frozen Transformer model during the fine-tuning stage.\n\n**For stability**: Freezing the guidance encoder provides a stable target for the denoising model to align with during preference optimization, reducing fluctuations in performance.\n**For efficiency**: Fine-tuning only the smaller denoising model (MLP) is significantly faster and less computationally expensive.\n\n\n> 4. Additional analysis to assess the diversity of the recommended items.\n\nWe recognize that embedding space visualization techniques like t-SNE are ideal for illustrating the diversity of generated items. However, since we are unable to submit images during the rebuttal, we conducted additional experiments using the coverage@20 metric to analyze the diversity of our recommendations, which measures the proportion of unique items recommended across multiple users.\nThe experimental results presented in Table R1 indicate that both TA-Rec and DreamRec, which leverage diffusion models for generating recommendations, exhibit greater diversity compared to traditional recommenders like SASRec. This underscores the **advantages of diffusion models in achieving diverse recommendations through stochastic noise**.\nFurthermore, the recommendations from TA-Rec demonstrate comparable diversity to those from DreamRec, suggesting that **the inherent diversity benefits of diffusion models are preserved even with a one-step reverse process**. This is attributed to the diffusion mechanism of random noise injection at each step, along with the noise-to-item mapping in TA-Rec.\n\n**Table R1:**  Experiments on the diversity of diffusion recommenders.\n| Methods | YooChoose  | KuaiRec |   Zhihu |  \n| :---: | :---: | :---: | :---: | \n|  | Cover@20| Cover@20| Cover@20|\n|SASRec|0.1703|0.8368|0.7306|\n|DreamRec|0.2051|0.8426|0.7616|\n|ours|0.2042|0.8416|0.7609|\n\n\n> 5. Typos: the second-best baseline in Table 1 and typo \"training\" in Table 3\n \nThanks again for your thorough review and feedback on our paper. We will correct these typos in the paper and carefully examine the context to improve our clarity.\n\nWe sincerely hope that our response has adequately **addressed your concerns**. If so, we would greatly appreciate your consideration in **raising the score**. If there are any remaining concerns, **please let us know**, and we will **continue to actively address your comments** and **improve our work**."}]

Author rebuttal:

Meta-review: This paper proposes TA-Rec, a two-stage diffusion-based recommendation framework designed to address the efficiency–effectiveness trade-off induced by discretization error. The pretraining stage introduces Temporal Consistency Regularization (TCR) to smooth the denoising trajectory and enable one-step generation, while the fine-tuning stage employs Adaptive Preference Alignment (APA) to better align generation with user preferences. Theoretical analysis provides an error bound for accelerated inference, and extensive experiments show that TA-Rec achieves efficiency comparable to transformer recommenders while surpassing multi-step diffusion baselines in accuracy.

The strengths of the paper are that it identifies a practically important problem—balancing inference efficiency and accuracy in diffusion recommenders—which had not been systematically addressed before (Reviewer ptjv, Reviewer dntH). The proposed TCR and APA modules are well-motivated, with bounded-error analysis giving theoretical grounding (Reviewer J9pE, Reviewer NQjp). Empirical validation is thorough: the method consistently outperforms diffusion and non-diffusion baselines across multiple datasets, showing 100× faster inference than DreamRec and measurable accuracy gains (Reviewer dntH). Reviewers also highlighted that the paper is well-written and easy to follow (Reviewer ptjv, Reviewer NQjp, Reviewer dntH).

The weaknesses concern novelty and scope. Reviewer ptjv argued that TCR is closely related to existing consistency models and APA to DPO, questioning whether the work represents a conceptual advance rather than an adaptation. Reviewer J9pE initially questioned whether one-step denoising undermines the essence of diffusion and whether diversity benefits are preserved; this was later addressed with diversity experiments and theoretical clarification. Reviewer NQjp and Reviewer dntH raised concerns about dataset diversity, lack of computational complexity analysis, and limited ablations on negative sampling. These points were addressed in rebuttal with new experiments on three additional datasets, coverage@20 analysis for diversity, detailed complexity derivations, and sensitivity tests with hard and popularity-based negatives. Reviewer BncC noted possible computational overhead in small-scale settings and reliance on random negative sampling, which were clarified by showing negligible cost relative to inference and by reporting robustness to alternative sampling strategies. Nonetheless, questions remain about the degree of conceptual novelty and the realism of some theoretical assumptions (e.g., pretraining loss tending to zero in Theorem 1, as noted by Reviewer ptjv).

During discussion, most reviewers acknowledged that their concerns were satisfactorily addressed. Reviewer J9pE explicitly raised their score after rebuttal. Reviewer NQjp and Reviewer dntH maintained borderline scores despite the new evidence, citing remaining reservations about evaluation scope and training overhead. Reviewer ptjv maintained a borderline accept, emphasizing limited novelty, though recognizing the empirical strength and engineering effort.

Overall, the paper makes a technically solid contribution by formulating an important problem, designing a coherent two-stage solution with both theoretical and empirical support, and showing clear efficiency–effectiveness improvements. While the degree of novelty is debated, the balance of evidence indicates that the work advances the state of diffusion-based recommendation in a meaningful way.

Final Recommendation: Accept
Decision: Accept (poster)