Title: Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: User Behavior Prediction

Diffusion Models
Abstract: Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendation. This problem is critical for applications such as personalized commerce and adaptive content delivery, where anticipating a user’s complete action sequence enhances both satisfaction and business outcomes. We identify an essential limitation of existing paradigms: their inability to capture global, listwise dependencies among sequence items. To address this, we formulate User Behavior Trajectory Prediction (UBTP) as a new task setting that explicitly models longterm user preferences. We introduce Listwise Preference Diffusion Optimization (LPDO), a diffusion-based training framework that directly optimizes structured preferences over entire item sequences. LPDO incorporates a Plackett–Luce supervision signal and derives a tight variational lower bound aligned with listwise ranking likelihoods, enabling coherent preference generation across denoising steps and overcoming the independent-token assumption of prior diffusion methods. To rigorously evaluate multi-step prediction quality, we propose the task-specific metric: Sequential Match (SeqMatch), which measures exact trajectory agreement, and adopt Perplexity (PPL), which assesses probabilistic fidelity. Extensive experiments on real-world user behavior benchmarks demonstrate that LPDO consistently outperforms state-of-the-art baselines, establishing a new benchmark for structured preference learning with diffusion models.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the diffusion-based multi-step recommendation. The authors first define a User Behavior Trajectory Prediction (UBTP) task, which aims to predict the next $k$ items based on the user's historical interactions and the predicted $K$ candidate items at each timestep. The authors then propose a novel Listwise Preference Diffusion Optimization (LPDO) method, which consists of three components: (1) the denoising loss, (2) the regularization (prior matching) loss, and (3) the ListPref loss. Experiments on three datasets validate the effectiveness of LPDO for the UBTP task.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The authors propose a novel UBTP task that differs from the traditional next-item prediction task.\n- The authors introduce several new metrics, including SeqMatch@$K$, SeqHR@$K$, and SeqNDCG@$K$, to evaluate the performance of UBTP.\n- The proposed LPDO method is well-designed with rigorous theoretical derivation, achieving superior empirical performance.\n\n**Weaknesses:**\n\n- The motivation of the new UBTP task is not well justified. In other words, why the multi-step is better than the next-item prediction task?\n- The necessity of introducing the Plackett-Luce model is also not well justified. Why not simply use the Top-1 Luce's Choice model instead?\n- The performance of next-item prediction is not reported, while it is essentially a special case of UBTP with $k = 1$.\n- The baselines are not specifically designed for UBTP, and the comparison with existing methods is limited.\n- Lack of comprehensive comparison and discussion with existing methods, e.g., SASRec and DCRec.\n\nQUESTIONS:\n**Major Concerns:** I have some major concerns as follows:\n\n- **UBTP Task.** This paper introduces a new UBTP task, i.e., $K$-candidate recommendation at the next $k$ steps. While this task is considered novel, the motivation of introducing multi-step recommendation is not well justified. The authors argue that the next-item prediction tends to be short-sighted. However, the autoregressive nature of next-item prediction makes it scalable to multi-step recommendation.\n- **LPDO Method.** The proposed LPDO method is based on the Plackett-Luce model (7). However, I have some concerns: (i) Why not simply use the Top-1 Luce's Choice model? There is only one positive item at each step, and the signal from the 2 to $K$-th items is considered useless for the final objective (20). (ii) It seems that the LPDO method is dependent on $K$, i.e., the number of candidate items at each step. For different metrics, e.g., SeqNDCG@$K$, the $K$ should also vary accordingly. However, there is no description of how to set $K$ in the ListPref loss (11), nor any corresponding experiments.\n- **Next-item Prediction.** From my understanding, the next-item prediction is essentially a special case of UBTP with $k = 1$. Since this task is more widely studied, the authors should also report the performance of next-item prediction in the experiments.\n- **Baselines.** As this paper focuses on the UBTP task, the authors should also provide stronger baselines specifically designed for UBTP. For instance, SASRec can be easily extended to UBTP by incorporating the Plackett-Luce model instead of the Top-1 softmax. The multi-step recommendation can also be achieved in SASRec by changing the embedding size from $d$ to $k \\times d$ and predicting the next $k$ items at each step. These baselines should be compared against the proposed LPDO method.\n- **Discussion on Existing Methods.** The authors do not provide a comprehensive comparison with existing methods. I recommend the authors to compare LPDO with existing autoregressive recommenders (e.g., SASRec) and diffusion-based recommenders (e.g., PreferDiff, DCRec) on the UBTP task (with $k = 1$ and $k > 1$, respectively).\n\n**Minor Comments:** There are some minor issues/questions that do not affect the overall quality of the paper, including:\n\n- Line 237: There are only three datasets in Table 1, but the text mentions four datasets.\n- Line 466: Some typos of $\\mathcal{H}$ in Appendix D.3.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article proposes a novel diffusion based framework LPDO (List Preference Diffusion Optimization) for user behavior trajectory prediction, which uses a generative paradigm to predict users' future interaction trajectories. The author believes that existing diffusion based recommendation models largely handle each position independently, thereby ignoring global list dependencies. The LPDO proposed in this article addresses this limitation by directly incorporating Plackett Luce list based ranking targets into diffusion ELBO, thereby aligning the generation process with trajectory level preferences. It also introduced a new metric, SeqMatch, for rigorous trajectory level evaluation and conducted extensive experiments, achieving the most significant results compared to strong baselines on four datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. The ELBO formulation incorporating Plackett-Luce likelihood provides a principled bridge between generative denoising and listwise ranking. The derivation of the tight variational bound is a notable theoretical contribution.\n2. The List Preference Diffusion Optimization (LPDO) framework proposed in this article is innovative in integrating list preference information into diffusion models. This method aligns the backpropagation process with the list ranking target, which is a new approach to improve the consistency and accuracy of predicting user behavior trajectories.\n3. LPDO achieves ​faster convergence and enables ​single-step inference​, reducing inference latency.\n\nWeaknesses:\n1. From Figure 3 (b), it is shown that the prediction results for long-term goals are similar to those for short-term goals, and LPDO shows a similar decrease in this prediction result compared to other sequence recommendation methods (pos1-pos5). This does not seem to reflect the superiority of LPDO in modeling users' long-term preferences compared to other methods.\n2. The expression in the paper is not clear enough, such as the \"UBTM\" in line 149, which is not explained in the entire text.\n3. The experimental results of various models at different prediction lengths are shown in Table 1. LPDO did not show a significant increase in advantages compared to other models at longer lengths (len=10) and shorter lengths (len=5)\n4. ​Hyperparameter Sensitivity: Performance heavily depends on manual tuning of loss weight λ and penalty γ. Optimal γ varies across datasets (0.0–0.8), raising generalizability questions. An adaptive tuning strategy or ablation on sensitivity is missing.\n5. The model requires ​fixed-length trajectories during training​. Real-world sequences are variable-length, limiting applicability to open-ended scenarios.\n2. Why is it that setting the diffusion step size to 50 and the sampling step size to 1 can yield very significant results?\n\nQUESTIONS:\nPlease refer to Weaknesses section.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work tackles the complex problem of forecasting user behaviour trajectories multiple steps into the future, a problem distinct from regular sequential recommendation. Generative recommendation with diffusion models is becoming more and more prevalent across the recommender systems research community due to the ability of such models to model uncertainty and learn latent data distributions in order to produce diverse recommendations. However, diffusion models are typically currently limited to next-item recommendation settings due to their inability to account for the dynamic nature of user preferences across stages of a predicted user behaviour trajectory. Point-wise preference signals can be incorporated, but joint dependences and relative ordering of items is still ignored, two factors which considerably impact the quality and impact of a sequence of recommendations. This work aims to address these issues directly.\n\nThe first contribution provided by this work is the formalisation of this task (UBTP) of forecasting an ordered sequence of $k$ interactions. In this problem setting, the authors propose a novel approach termed LDPO which integrates Plackett-Luce ranking terms into the evidence lower bound (ELBO) of a non-autoregressive diffusion model in continuous latent space. Deriving this tractable lower bound which jointly handles reconstruction and listwise likelihoods, whilst simultaneously ensuring stable training and managing the generation fidelity-preference alignment trade-off is a non-trivial task, which the authors tackle through devising an end-to-end differentiable scoring head mapping denoised latents to logits and formulating a tight decomposition of the ELBO. Additionally, the authors also introduce a new metric, SeqMatch, which is specifically designed for evaluating multi-step recommendation quality. Specifically, SeqMatch@$k$, computes the percentage of test user trajectories where each item in the trajectory is present in the model’s top $k$ predictions for their respective positions.\n\nExperimental results are provided exploring the performance of LPDO compared to competing baselines and to other diffusion-based methods, with some additional studies performed on the impact of different hyperparameters on performance. Several large-scale, widely used recommendation benchmark datasets are used for evaluation, and a range of both traditional and generative recommendation algorithms are evaluated alongside LPDO. The authors find that their approach outperforms the selected baselines across the board and that LPDO converges faster than other diffusion-based methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n* Overall, the work is novel and well-motivated. Diffusion models may be gaining prevalence for large-scale recommendation, however tackling this problem of multi-step trajectory forecasting in a manner which properly accounts for the ordering and causal relationships between items in a given trajectory is a key step in ensuring these methods are more widely applicable in real-world scenarios. This work clearly appears to be a considerable step towards addressing this problem, and as such has potential for significant impact.\n* The paper itself is very well structured and clear throughout; aspects of the model formulation, derivation of the variational lower bound and the training procedure are all outlined concisely and clearly, and on inspection all the work appears sound and of high quality. Likewise, the evaluation of the model and the competing methods is presented in an easily parsed and engaging manner, with the key takeaways being clearly signposted and explained in depth.\n* The empirical results are very good, with the proposed technique in some cases providing very significant improvements over the competing baselines, across all of the datasets used for testing. The evaluation has been thoroughly carried out, with a range of different metrics used to show the superior performance of the proposed method.\n* A particular strength of this work is the choice by the authors to formulate not only the task being studied, but also a more relevant metric for evaluation. Often, having a clear target, problem statement and metric for improvement is the most important factor in driving forward progress in a certain area of research, and I think contributions such as this which attempt to provide these things can be highly impactful in the sense that they incentivise future work on the topic, driving further developments.\n\n**Weaknesses**\n* Ultimately, I have very few negative things to say about the paper. It’s very well constructed and presented, is novel, addresses a clear problem, and evidences the claims included with thorough evaluation. \n* One clear weakness is the lack of open-source code, as this will limit the impact of the paper given that it is more challenging for practitioners to use the technique, and more time consuming for researchers to pursue further work based on this approach. I appreciate that this may not be possible for certain reasons, however it does certainly limit the impact.\n* As mentioned in the questions section below, some further clarity about which of the experimental results are statistically significant would be appreciated, as this is not immediately clear from the results tables.\n\nQUESTIONS:\n**Q1** - As mentioned above, I have very little to ask as the paper is very thorough, and I recommend acceptance. One question I do have is whether the authors could expand further on the rationale behind not providing open-source access to the code for the implementation? The high potential for impact that this paper has could be limited somewhat by not providing a base implementation for further research to easily progress onwards from.\n\n**Q2** - The authors note in the checklist that the improvements in performance are statistically significant, but this is not included in the main text. Could you confirm that this is the case for all experiments conducted, and if not, could these cases be flagged in the results table (e.g. via an asterisk or simply un-bolding the result)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n* This paper considers recommender systems and has three contributions:\n    * It proposes a new problem, User Behavior Trajectory Prediction (UBTP), of predicting a sequence of a user's preferred items in the future k time steps by using this user's history of viewed items in the past n time steps.\n    * It adopts a diffusion model to do such prediction, proposing a method called Listwise Preference Diffusion Optimization (LPDO). The diffusion model's output is k embeddings, and each embedding is a vector of utilities for all the items. For each of the k future time steps, the system predicts the user's top-K items according to the output of the diffusion model.\n    * It proposes a new metric to evaluate the performance of UBTP, by checking how often the top-K predicted items actually contain the true most-preferred items.\n* This paper evaluated LPDO using 4 real-world datasets, where k is set to 3, 5, or 10, against 7 baselines. It shows that LPDO outperformed significantly all of them.\n\nSTRENGTHS AND WEAKNESSES:\n* Strength\n    * The proposed problem is well-motivated.\n    * The proposed metric aligns well with the proposed problem.\n    * The proposed algorithm empirically significantly outperformed baselines.\n* Weakness\n    * One concern is that the key innovation and insights behind the proposed method, LPDO, are a bit unclear, making it hard to fully appreciate its technical novelty. For more details, please refer to the \"Questions\" section later in the review.\n    * Another concern is that the empirical analysis is a bit unclear about the key contributor to the improved performance of the proposed method. I think there could be a couple of factors why the proposed method outperformed previous methods: (1) it tries to predict k future time steps, rather than 1 time step; (2) its novel loss function; (3) it uses the causal transformer. It could be helpful to better understand the contribution of these factors. A better understanding of this question could allow the readers to appreciate the technical novelty more.\n        * Also, related to factor (1), since the proposed method predicts a longer future, would it require more data or a longer time to train? Understanding this question could allow potential users to better decide when to use the proposed method.\n* Minor\n    * Line 119: \"target item\" is confusing, as it makes me confused whether this refers to the single-item prediction task or the proposed UBTP task, which tries to predict k items.\n    * Line 158: `Equation (24)` is mistakenly referred I think.\n    * Line 190: `Section 3` is mistakenly referred I think.\n\nQUESTIONS:\n* Line 53-59 tries to explain why the proposed method LPDO outperforms previous methods, but are too technical to fully grasp in the introduction section. Fig.1 clearly explains the insight of why Fig.1(c) is beneficial, but is not echoed by the technical presentation of LPDO. It could be helpful in the later section of the paper to explain why LPDO's new loss function can achieve Fig.1(c), improving upon previous work in Fig.1(b).\n* Line 153 mentioned that the paper follows [18] to also include history H as part of X0, which the model is trying to generate. I am wondering why this is helpful. Could you provide some insights to explain this design consideration?\n* Line 157 says that pθ(X_{t−1} | Xt, H). I thought that for each t, the history H is already a part of Xt. So why do we have the extra \"H\" in `pθ(X_{t−1} | Xt, H)`?\n* Eq.11 introduces the loss for listwise preference. However, it seems that `L_ListPref` is defined for a particular future time step j? How would the system combine all the k future time steps in the loss, considering that the system is trying to capture the causal relation between those k future time steps?\n* Eq.16 contains both reconstruction loss and list preference loss. I thought the reconstruction loss already contains the list preference loss, as the reconstruction loss is about the loss of the reconstructed k future step predictions?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}