Uncertainty-aware Guided Diffusion for Missing Data in Sequential Recommendation

Authors: Wenyu Mao, Zhengyi Yang, Jiancan Wu, Haozhe Liu, Yancheng Yuan, Xiang Wang, Xiangnan He
Primary area: generative models
Keywords: diffusion models, recommender systems, missing data

Abstract:
Denoising diffusion models (DDMs) have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences. However, the quality of guidance is often compromised by the unpredictable missing data in the observed sequence, leading to suboptimal item generation. To tackle this challenge, we propose a novel uncertainty-aware guided diffusion model (DreamMiss) to alleviate the influence of missing data. The core of DreamMiss is the utilization of a dual-side Thompson sampling (DTS) strategy, which simulates the stochastical mechanism of missing data without disrupting preference evolution. Specifically, we first define dual-side probability models to capture user preference evolution, taking into account both local item continuity and global sequence stability. We then strategically remove items based on these two models with DTS, creating uncertainty-aware guidance for DDMs to generate oracle items. This can achieve DDMs’ consistency regularization, enabling them to resile against uncertain missing data. Additionally, to accelerate sampling in the reverse process, DreamMiss is implemented under the framework of denoising diffusion implicit models (DDIM). Extensive experimental results show that DreamMiss significantly outperforms baselines in sequential recommendation.

Decision: reject
Reviewer scores: 6, 5, 10, 5

Reviews and discussion:
<thread>
creation date:
1730271346505

thread_type:
official_review

<thread_object>
creator: reviewer_vkfh

<content>
summary:
This paper proposes a dual-side Thompson sampling (DTS) strategy to simulate the stochastical mechanism of missing data, and integrates it into denoising diffusion models for sequential recommendation. Extensive experimental results show that DreamMiss significantly outperforms baselines.

strengths:
1. The experimental results are sound.
2. The paper is overall well written.

weaknesses:
1. The missing data issue is not well discussed. Thompson sampling can not tackle all the missing data issues. How the proposed model can address which kind of missing data should be discussed in the paper.
2. The rationale of accelerated sampling is not well explained.
2. The hypothesis of stability scores is not reasonable. In recommendation model training, data are often shuffled to remove the dependency among samples. However, the stability scores seems the opposite way; it used the batch information for sampling. It contradicts with the traditional way.

questions:
1. The line 7 of Algorithm 1 seems a bit different than other DDPM models. There's no square root above the $(1 - \alpha_{\tau_s})$. Is it a typo?
2. Can the authors explain why accelerated sampling works? It seems to reduce thousands of iterations to less than 100 rounds, which is a huge improvement.
3. Based on the performance of Dreasmiss, can we say the DreamRec has a overfitting problem? With much more rounds of iteration DreamRec has inferior performance.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730346001361

thread_type:
official_review

<thread_object>
creator: reviewer_xlhr

<content>
summary:
Sorry, I am not familiar with this research area so I can't give a valuable score. Please overlook my score.

strengths:
Sorry, I am not familiar with this research area so I can't give a valuable score. Please overlook my score.

weaknesses:
Sorry, I am not familiar with this research area so I can't give a valuable score. Please overlook my score.

questions:
Sorry, I am not familiar with this research area so I can't give a valuable score. Please overlook my score.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730630640664

thread_type:
official_review

<thread_object>
creator: reviewer_fppx

<content>
summary:
The paper proposes DreamMiss, a novel approach to handle missing data in sequential recommendation systems using denoising diffusion models (DDMs). The key innovation is a dual-side Thompson sampling (DTS) strategy that simulates missing data stochastically while preserving user preference patterns. The approach uses two probability models: 1) A local model that captures continuity between adjacent items A global model that evaluates sequence stability. 2) DreamMiss is implemented using denoising diffusion implicit models (DDIM) for faster sampling and achieves consistency regularization through uncertainty-aware guidance.

strengths:
The paper is generally well-written.
The problem of missing data is crucial for developing sequential recommenders.

weaknesses:
W1. Conceptual Clarity and Methodology Concerns.
The paper lacks clear theoretical justification for how generating additional missing data helps address the original missing data problem.
The approach of simulating missing data from already incomplete sequences raises questions about potential error propagation.
The methodology appears counterintuitive compared to traditional approaches that aim to recover or compensate for missing data.
The paper would benefit from a more rigorous theoretical analysis of why this approach is superior to data completion methods.

W2. Limited Validation of Dual-side Thompson Sampling (DTS).
The paper does not sufficiently justify why DTS is specifically effective for diffusion-based sequential recommenders.
There is inadequate theoretical analysis or empirical validation of the reliability of the continuity and stability metrics.
The generalizability of DTS to other recommendation architectures needs more thorough investigation.
The robustness of the probability models used in DTS requires more comprehensive validation.

W3. Incomplete Baseline Comparisons.
Notable omissions of important state-of-the-art baselines, particularly:
[1] Lin, Y.; Wang, C.; Chen, Z.; Ren, Z.; Xin, X.; Yan, Q.; de Rijke, M.; Cheng, X.; and Ren, P. A Self-Correcting Sequential Recommender. In TheWebConf 2023.
[2] Zhang, C.; Han, Q.; Chen, R.; Zhao, X.; Tang, P.; and Song, H. SSDRec: Self-Augmented Sequence Denoising
for Sequential Recommendation. In ICDE 2024.
These omissions make it difficult to fully assess the comparative advantages of the proposed method.
The evaluation would be more convincing with a more comprehensive comparison against recent approaches.

W4. Scalability Limitations.
The computational complexity of the proposed method may limit its practical applications.
Insufficient discussion of performance on large-scale recommendation systems.
Limited analysis of computational resource requirements for real-world deployment.
Need for more detailed discussion of potential optimization strategies for larger datasets.

W5. Dataset Limitations.
The evaluation relies on relatively small-scale datasets.
Questions about generalizability to larger, more complex real-world recommendation scenarios.
Need for validation on more diverse and larger-scale datasets.
Limited demonstration of effectiveness across different domains and data distributions.

questions:
Please refer to the Weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730712195678

thread_type:
official_review

<thread_object>
creator: reviewer_jqjs

<content>
summary:
The proposed method aims to tackle overlooking the missing data within the guidance for diffusion models. Specifically, they first detect the missing data by constructing local and global models and checking the continuity and stability. Based on the detected missing data, the authors then train diffusion models with uncertainty-aware guidance. Extensive results are provided to support the effectiveness.

strengths:
- This paper is well-structured and easy to follow.
- The proposed method is well-motivated and novel. 
- The details of the experiments are revealed, and the code is released, which will ease the reproducibility of this paper.

weaknesses:
- Some typos exist. For example, the first character in line 72 should be capitalized.
- This paper is not well-motivated. Why is Thompson sampling the best choice to derive the guide signal for a diffusion-based recommendation? Besides, no related experiments can verify the best of the Thompson sampling strategy compared to other sampling strategies.
- I noticed that the scale of datasets used in the experiments is relatively small, with interaction counts of fewer than one million. I recommend that the authors conduct experiments on larger datasets to further demonstrate the effectiveness of the proposed method, or the method's efficiency may be questioned.

questions:
All my questions have been included in the weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732356074429

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response

comment:
Dear Reviewers/ACs/SACs/PCs,

We would like to summarize the strengths of this work acknowledged by reviewers, the contributions of our work, and the revision and responses we have made to address all the reviewers’ concerns.

**Strengths**:
- Motivation and Novelty: well-motivated (`Reviewer jqjS`), novel (`Reviewer jqjS`), the problem of missing data is crucial (`Reviewer fpPx`)

- Presentation: well-structured and easy to follow (`Reviewer jqjS`), generally well-written (`Reviewer fpPx`), overall well-written (`Reviewer VkFH`)

- Experimental effectiveness: ease the reproducibility (`Reviewer jqjS`), experimental results are sound (`Reviewer VkFH`)


**Contributions of our work:** 
 - We propose uncertainty-aware guided diffusion for the sequential recommendation, addressing the issue of uncertain missing data. 
 - We design a novel Dual-side Thompson Sampling strategy to simulate uncertain missing data, which samples based on two probability models (i.e., item continuity and sequence stability). 
 - Theoretical analysis through extrapolation and consistency regularization justifies the effectiveness of DreamMiss, supported by extensive experiments.


**Responses and revision** (highlighted in red text in the PDF) to address these reviewers’ concerns are **summarized** as follows:

- `Reviewer jqjS`:
  - **Motivation for TS:** We have clarified the advantages of TS and compared it with other sampling strategies. Through additional experiments, we have demonstrated the superiority of DTS and claim it as a well-performing implementation of our idea rather than the best sampling strategy. We have included the clarification and experiments in Appendix B.2 and F.5 of our revision.

  - **Experiments on larger datasets:** We have conducted additional experiments using the Steam dataset, one of the largest datasets commonly utilized in research. The results further validate the effectiveness of DreamMiss. We have included the experiments in Appendix F.1 of our revision.


- `Reviewer fpPx`:
   - **Theoretical justification for the method of simulation:** We have clarified why we simulate missing data instead of compensating for it, supported by experimental results comparing three methods: simulating missing data with DreamMiss, using observed sequences with DreamRec, and compensating for missing data with DreamImp. Additionally, we provide a theoretical analysis of our method through extrapolation and consistency regularization. We include the theoretical analysis and experiments in Appendix B.1, B.3, and F.3. 
   - **Validation of Dual-side Thompson Sampling (DTS):** We have clarified the advantages of DTS and diffusion models. To validate these claims, we have conducted additional experiments to assess the generalizability of DTS to other recommenders and compared different sampling strategies with DTS. Moreover, we have proposed additional metrics to evaluate the reliability of continuity and stability, as well as the robustness of the probability models through experiments. We have included the explanation and experiments in Sections 4.3 and Appendix B.2, F.4, and F.5 in our revision.
   - **More Baselines and more datasets:** We have conducted experiments on two additional baselines and expanded our methods to include larger datasets (Steam) as well as more diverse datasets (Beauty and Toys). We have included additional experimental results in Table 1 and Appendix F.1 of our revision.
   - **Scalability Limitations:** We have analyzed the computational complexity and computational resource requirements of DreamMiss in comparison to other recommenders. Additionally, we have discussed the potential optimization strategies for larger datasets. We include these analyses in Appendix F.7 of our revision.

- `Reviewer VkFH`:
  - **Clarification for missing data issue:** We have discussed the missing data issue that DTS can address and included the discussion in Appendix F.3 of our revision.
  - **Rationale of accelerated sampling for DDIM:** We have provided a more detailed explanation of the rationale behind DDIM's accelerated sampling and compared it with DDPM. We include it in Appendix C of our revision.
  - **Reasonablility of stability scores:** We have clarified the calculation of stability scores, which are based on item continuity scores within a sequence and align with traditional methods.

We warmly encourage you to review the results in the revised manuscript. Hope our response and additional experiments could address your concerns!

Once again, we deeply appreciate the time and expertise you have shared with us. Your encouraging feedback motivates us to continue advancing this work for the broader community, and we are more than happy to add clarifications to address any additional recommendations and reviews from you！

Best regards,

Authors of Paper 7669
</content>
</thread_object>

</thread>


Metareview:
metareview:
The proposed method addresses the challenge of overlooked missing data in the guidance of diffusion models. The approach begins by detecting missing data through the construction of local and global models, assessing continuity and stability to identify gaps. Using the detected missing data, the authors train diffusion models with uncertainty-aware guidance to improve robustness. Extensive experimental results demonstrate the effectiveness of the proposed method.

Positive points:
+ This paper is well-written and organized well.
+ The studied problem is important.

Negative points:
- The model lacks clear theoretical justification (why the model can work)
- The baselines are not sufficient.
- The proposed method can be not scalable.