A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation

Authors: Huimin Zeng, Xiaojie Wang, Anoop Jain, Zhicheng Dou, Dong Wang
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: sequential recommendation, non-contrastive learning

Abstract:
Contrastive Learning (CL) proves to be effective for learning generalizable user representations in Sequential Recommendation (SR), but it suffers from high computational costs due to its reliance on negative samples. To overcome this limitation, we propose the first Non-Contrastive Learning (NCL) framework for SR, which eliminates computational overhead of identifying and generating negative
samples. However, without negative samples, it is challenging to learn uniform representations from only positive samples, which is prone to representation collapse. Furthermore, the alignment of the learned representations may be substantially compromised because existing ad-hoc augmentations can produce positive samples that have inconsistent user preferences. To tackle these challenges, we design a novel preference-preserving profile generation method to produce high-quality positive samples for non-contrastive training. Inspired by differential privacy, our approach creates augmented user profiles that exhibit high diversity while provably retaining consistent user preferences. With larger diversity and consistency of the positive samples, our NCL framework significantly enhances the alignment and uniformity of the learned representations, which contributes to better generalization. The experimental results on various benchmark datasets and model architectures demonstrate the effectiveness of the proposed method. Finally, our investigations reveal that both uniformity and alignment play a vital role in improving generalization for SR. Interestingly, in our data-sparse setting, alignment is usually more important than uniformity.

Decision: accept (poster)
Reviewer scores: 6, 6, 6, 8

Reviews and discussion:
<thread>
creation date:
1729938840971

thread_type:
official_review

<thread_object>
creator: reviewer_a3ie

<content>
summary:
The paper introduces the NCL-SR, which is designed to reduce the computational burden found in traditional Contrastive Learning apporaches by eliminating the need for negative sampling. CL methods generally require negative samples to avoid representation collapse, but this comes with high computational costs. The main contribution of NCL-SR is its preference-preserving profile generation technique, which uses differential privacy to create diverse yet consistent positive user profile samples that retain the user’s preferences. This helps to overcome challenges such as representation collapse and preference inconsistency.

strengths:
- This paper solves a important question in traditional SR field, that CL approaches cost quite a lot computational resources. By adopting the profile augmentation method proposed in this paper, the performance of SR can even higher than those traditional CL methods.

- The authors provides theoritial guarantees for the uniformity and alignment.

weaknesses:
- Since the main motivation of NCL is to reduce the cost of traditional CL methods, I believe the efficiency study is needed.
- When comparing with other SR models (besides CL methods), I believe they should also be implemented with e5 for a fairer comparison.

questions:
- The authors claim that the user profile generation is inspired by differential privacy, I wonder how it inspired the proposed framework in details? Maybe some addtional literature review might help to explain.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730021930910

thread_type:
official_review

<thread_object>
creator: reviewer_mpcm

<content>
summary:
In this paper, the authors propose the first Non-Contrastive Learning (NCL) framework for Sequential Recommendation (SR). The authors design a novel preference-preserving profile generation method to produce high-quality positive samples for non-contrastive training. The paper is easy to follow and well-organized. The authors conduct extensive experiments to validate the efficacy of the proposed NCL-SR.

strengths:
S1: The paper is easy to follow and well-organized.

S2: The authors provide theorem analysis for the proposed method which is novel and interesting to me.

S3: The authors conduct extensive experiments to validate the efficacy of the proposed NCL-SR.

weaknesses:
W1: Some illustrations should be improved. For example, the authors should motivate why you choose Matrix Cross Entropy (MCE) against some other methods (e.g., Barlow Twin, MEC and DirectAU) for contrastive learning? 

W2: Some method details are missing. For example, the calculation of $C(Z,Z’)$ in Eq.(9) should be provided to make the paper more readable.

W3: Since you need to figure out the eigenvalue of $\bm{V}$ in Eq.(9), how about the time complexity of your proposed method?

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

</thread>
<thread>
creation date:
1730380770935

thread_type:
official_review

<thread_object>
creator: reviewer_eecc

<content>
summary:
The authors propose a Non-Contrastive Learning framework for Sequential Recommendation powered by preference preserving user profile generation: NCL-SR. The NCL-SR framework eliminates the computational overhead of identifying and generating negative samples in CL. The experimental results on various benchmark datasets and model architectures demonstrate the effectiveness of the NCL-SR method.

strengths:
+ The methodology of this paper is technically sound. The method itself is somewhat novel to me.

+ To my best of knowledge, it is the first attempt to utilize matrix cross entropy in the recommendation system.

+ The proposed NCL-SR achieves much better performance against other baseline models.

weaknesses:
+ Fig.1. needs to be polished. For instance, it is unclear for me what is the plot meaning of the exponential mechanism with polygons in Fig.1? 

+ Why do you set the utility score as $\Delta_u = e - 1/e$? It is unclear for me. Could you please kindly provide some insights on that? 

+ Could different values of $\gamma$ affect the model performance? 

+ Some similar papers should be cited or discussed, e.g., [1] and [2].

Ref:

[1] DDGHM: Dual dynamic graph with hybrid metric training for cross-domain sequential recommendation

[2] Enhancing hierarchy-aware graph networks with deep dual clustering for session-based recommendation

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

</thread>
<thread>
creation date:
1730562943199

thread_type:
official_review

<thread_object>
creator: reviewer_u9ds

<content>
summary:
Summary:
This paper addresses the issue of high computational cost in contrastive learning (CL)-based sequential recommendation. Innovatively, it introduces non-contrastive learning into sequential recommendation. To tackle the representation collapse and alignment problems caused by solely relying on positive samples, a novel user preference-preserving profile generation method is proposed for the Non-Contrastive Learning framework for sequential recommendation (NCL-SR). NCL-SR is capable of learning generalized and robust user representations for sequential recommendation. Experimental results demonstrate that NCL-SR outperforms traditional SR models and CL-based SR models in terms of performance.

strengths:
Strength：
1.This paper innovatively proposes a non-contrastive learning framework for sequential recommendation.
2.Inspired by Differential Privacy (DP), a theoretically guaranteed user preference-preserving data augmentation method is proposed to address representation collapse and preference inconsistency issues.
3.Two novel loss calculation methods are introduced.
4.Extensive experiments are conducted to verify the effectiveness, and the roles of feature alignment and uniformity in sequential recommendation are analyzed.

weaknesses:
Limitation：
1.What does "s()" represent in Eq. 1? What does "f()" represent in Eq. 4?
2.What do the three polygons in the upper part of Fig. 1 represent? Why is there no corresponding polygon for the third red text?
3.During the construction of candidate user profiles, a item-level approach is adopted. How is the number of perturbations determined? Why weren't further experiments conducted to verify the effect of perturbation quantity on recommendation performance?
4.What is the split ratio of the training, validation, and test sets? Why wasn't it explicitly stated?
5.The motivation of this paper is to address the high computational cost and memory consumption in negative sample extraction of CL in SR. Why weren't further experiments conducted to verify the effect of NCL-SR on computational cost and memory consumption?
6.Further refinement of the text is needed. For instance, what is the relationship between “NCL SR” and “NCL-SR” in line 178? The term "differential privacy" in line 210 is in lowercase, while "Differential Privacy" in line 215 is in uppercase. Consistency should be maintained.

questions:
Limitation：
1.What does "s()" represent in Eq. 1? What does "f()" represent in Eq. 4?
2.What do the three polygons in the upper part of Fig. 1 represent? Why is there no corresponding polygon for the third red text?
3.During the construction of candidate user profiles, a item-level approach is adopted. How is the number of perturbations determined? Why weren't further experiments conducted to verify the effect of perturbation quantity on recommendation performance?
4.What is the split ratio of the training, validation, and test sets? Why wasn't it explicitly stated?
5.The motivation of this paper is to address the high computational cost and memory consumption in negative sample extraction of CL in SR. Why weren't further experiments conducted to verify the effect of NCL-SR on computational cost and memory consumption?
6.Further refinement of the text is needed. For instance, what is the relationship between “NCL SR” and “NCL-SR” in line 178? The term "differential privacy" in line 210 is in lowercase, while "Differential Privacy" in line 215 is in uppercase. Consistency should be maintained.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732665937903

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response and Summary of Paper Updates

comment:
We thank all reviewers for their valuable and insightful feedback. We are particularly grateful that the reviewers found our proposed approach novel (u9dS, EEcc, MpcM), and recognized our studied problem as important (A3iE). The reviewers also appreciated our theoretical analysis (u9dS, MpcM, A3iE), considered our work technically sound (EEcc), and commended the extensive experiments we conducted (u9dS, EEcc, MpcM, A3iE).

According to the suggestions and comments from the reviewers, we conduct a series of additional experiments and include the results in the revised paper. We also add more clarifications in our paper to address the questions raised by the reviewers.

Below is a summary of paper updates, corresponding to reviewers' comments.

- **Regarding the questions from Reviewer u9dS:**
    - **W1**: we add the definition of $s$ and $f$ at line 173 and line 247, respectively.
    - **W2**: we add the explanation for Fig 1 in Section 4.1 (line 295 - 302). 
    - **W3**: we add the experiments w.r.t. the number of perturbations in Section 5.5 and Appendix E. 
    - **W4**: we add the clarification about the data split ratio in Section 5.1
    - **W5**: we add the memory consumption evaluation of CL and NCL methods in Section 5.3 and Appendix G.
    - **W6**: we fix the typos in our paper and make the terminology consistent in line 178 and line 210.
- **Regarding the questions from Reviewer EEcc:**
    - **W1**: we add the explanation for Fig 1 in Section 4.1 (line 295 - 302).
    - **W2**: we add the clarification about the sensitivity score of the utility score at line 257.
    - **W3**: we add the clarification about the effect of $\gamma$ in Section 5.5 and Appendix E.
    - **W4**: we update Section 2 by citing the papers the reviewer recommended.
- **Regarding the question from Reviewer MpcM:**
    - **W1**: we add a motivation of choosing MCE over other NCL/CL methods in line 324 - 327.
    - **W2**: we add the equation for computing $C(Z, Z^{'})$ in Line 339.
    - **W3**: we add the clarification about computing $\log V$ in Section 4.2 and Appendix G.
- **Regarding the question from Reviewer A3iE:**
    - **W1**: we add the memory consumption evaluation of CL and NCL methods in Section 5.3 and Appendix G.
    - **W2**: we add the experiments for implementing SR models with E5 in Section 5.2 and Appendix F. 
    - **Q1**: we add a detailed discussion between our method and DP in Section 4.1 and Appendix A.


We again greatly appreciate all reviewers' suggestions. We hope the updated results and our responses have addressed the questions that reviewers have. Please let us know if there are any other questions or suggestions.

Thanks,

Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper titled "A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation" introduces a novel approach to sequential recommendation (SR) by addressing the computational challenges inherent in traditional contrastive learning (CL) methods. The authors propose the first non-contrastive learning (NCL) framework tailored for SR, which eliminates the reliance on negative samples and thus reduces computational overhead. To mitigate the risk of representation collapse—common in the absence of negative samples—the authors design a preference-preserving profile generation method inspired by differential privacy. This method creates diverse and consistent user profiles that improve the alignment and uniformity of the learned representations. Experimental results across various benchmark datasets and architectures demonstrate the method's effectiveness, surpassing traditional CL-based SR methods and baseline SR models. The theoretical analysis of alignment and uniformity is another key contribution, with insights into their interplay in improving generalization.

The strengths of the paper are manifold. First, the work presents a clear innovation by adapting NCL to sequential recommendation, which, to my knowledge, has not been explored before. Second, the preference-preserving profile generation method offers both theoretical guarantees and practical utility, ensuring high-quality positive samples for non-contrastive training. Third, the paper's experimental rigor is commendable, with results showcasing significant improvements over existing methods. Additionally, the authors provide a thorough analysis of alignment and uniformity, which offers valuable insights for the research community. Finally, the reviewers highlighted the technical soundness and novelty of the proposed approach, further validating its scientific merit.

Despite its merits, the paper has some weaknesses. While the methodology is solid, the reviewers noted areas requiring clarification or refinement. For example, Reviewer u9dS pointed out inconsistencies in text presentation and the lack of clarity in defining certain terms and equations, such as "s()" and "f()". Reviewer EEcc requested a deeper explanation for specific design choices, like the utility score formulation and the plotting details in Figure 1. Reviewer MpcM raised concerns about the time complexity of eigenvalue calculations and the motivation behind choosing Matrix Cross Entropy (MCE) over other methods. Additionally, Reviewer A3iE suggested more detailed discussions on the connection between differential privacy and the proposed profile generation mechanism, as well as efficiency studies on computational cost and memory consumption.

The authors addressed these concerns effectively during the rebuttal period. They provided clarifications and new content in the revised paper, such as clearer definitions of key terms, additional experimental results on memory consumption and computational efficiency, and expanded discussions on related works and design decisions. The detailed response to Reviewer u9dS's questions, including new sensitivity analyses and performance evaluations, strengthens the paper's rigor. Similarly, the explanations offered to Reviewers EEcc, MpcM, and A3iE enhance the paper's presentation and address the raised points adequately.

The decision to recommend acceptance is based on several critical factors. First, the paper's contributions are both novel and impactful, addressing a significant limitation in sequential recommendation systems. The proposed method not only improves efficiency but also enhances representation learning quality, as demonstrated empirically and theoretically. Second, the authors' engagement during the rebuttal period and the substantial improvements made to the manuscript indicate a commitment to high-quality research. Lastly, while the reviewers raised some valid concerns, the authors addressed these comprehensively, leaving no significant gaps in the work.