Title: On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Models

Recommender System

Preference Optimization
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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).\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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\".\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 1, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n1. The experimental dataset may lack diversity, more experimental dataset would be prefered.\n\nLIMITATIONS:\nThe TCR module necessitates executing the denoising model twice per training iteration for adjacent steps, increasing computational overhead during the pretraining stage.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n- **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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nWhile the authors have thoroughly evaluated the effectiveness and efficiency of their proposed approach, there is no discussion of potential negative societal impacts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\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.\n\nQUESTIONS:\n1. 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​\n\nLIMITATIONS:\n1. ​​Dataset diversity insufficiency​​, limiting claims of broad applicability.\n​​2. Omitted computational complexity analysis​​, hindering practical adoption assessment.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nSee Weaknesses and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-15"}