## Reviewer 1 **Relevance to SIGIR:** 4 (good) **Originality of the work:** 2 (poor) ### Originality of the work (comments) The motivation behind the proposed approach is not clear. While it appears to aim at combining contrasting views and hard negative samples, the necessity and advantages of this combination are not clear. **Technical soundness:** 2 (poor) ### Technical soundness (comments) In comparing with baselines, the enhancements achieved by the proposed SCONE appear marginal, particularly on datasets such as Amazon CDs and Yelp2018. The authors need to conduct a significance test to ascertain the statistical significance of these improvements. **Quality of presentation:** 4 (good) **Quality of presentation (comments):** This work is well-written. **Related work:** 4 (good) **Perceived significance or potential impact of the contribution:** 3 (fair) ### Strengths - The authors generate samples for each specific purpose via stochastic sampling. - The authors summarize two directions' works: contrastive learning and hard negative sampling in CF. - The authors summarize two directions' works: contrastive learning and hard negative sampling in CF. - Extensive experiments on six real-world datasets. ### Weaknesses - The motivation behind the proposed approach is not clear. While it appears to aim at combining contrasting views and hard negative samples, the necessity and advantages of this combination are not clear. - In comparing with baselines, the enhancements achieved by the proposed SCONE appear marginal, particularly on datasets such as Amazon CDs and Yelp2018. The authors need to conduct a significance test to ascertain the statistical significance of these improvements. - The authors highlight data sparsity and negative sampling as prevalent issues. However, it's important to note that negative sampling is a strategy or function rather than a problem in itself. Clarification on this distinction would improve the precision of the discussion. **Overall recommendation:** -1 (weak reject) **Detailed comments to authors:** Please refer to the weaknesses. ## Reviewer 2 **Relevance to SIGIR:** 5 (excellent) **Originality of the work:** 3 (fair) ### Originality of the work (comments) 1. The authors claim that the combination of contrastive learning and negative sampling is novel. However, SGL and SimGCL mentioned in the paper also combine the two views. 2. The main modules of the model including LightGCN and SGM are proposed by existing work. 3. The methods for contrastive learning and negative sampling may be novel. However, the authors fail to articulate the advantages of their methods over existing methods. **Technical soundness:** 3 (fair) ### Technical soundness (comments) Advantages: 1. The authors provide the code and data. 2. The experiments are adequate. The only regret is that the authors do not replace their contrastive learning and negative sampling methods with other existing methods to verify the validity of their proposed methods. Disadvantages: 1. Although the problem in recommendation systems is well known, the author should give the definition of the problem in the paper. 2. Table 4 claims that SGL and SimGCL belong to the methods using contrastive learning. However, Table 2 claims that the two methods belong to the methods using contrastive views and negative samples. 3. Table 1 claim that SGM belongs to the domain of recommendation, but SGM is proposed to solve the image generation problem. **Quality of presentation:** 3 (fair) ### Quality of presentation (comments) I can understand the ideas and methods proposed by the authors. However, there are many inconsistencies in the paper, and the authors need to revise it carefully to achieve rigor. **Related work:** 4 (good) **Perceived significance or potential impact of the contribution:** 4 (good) ### Strengths 1. The idea of combining the contrastive learning and negative sampling methods is novel. 2. The authors provide the code and data. 3. The experiments are adequate. The only regret is that the authors do not replace their contrastive learning and negative sampling methods with other existing methods to verify the validity of their proposed methods. ### Weaknesses 1. The authors claim that the combination of contrastive learning and negative sampling is novel. However, SGL and SimGCL mentioned in the paper also combine the two views. 2. The main modules of the model including LightGCN and SGM are proposed by existing work. 3. The methods for contrastive learning and negative sampling may be novel. However, the authors fail to articulate the advantages of their methods over existing methods. 4. Although the problem in recommendation systems is well known, the author should give the definition of the problem in the paper. 5. There are many inconsistencies in the paper, and the authors need to revise it carefully to achieve rigor. For example, 1) Table 4 claims that SGL and SimGCL belong to the methods using contrastive learning. However, Table 2 claims that the two methods belong to the methods using contrastive views and negative samples. 2) Table 1 claim that SGM belongs to the domain of recommendation, but SGM is proposed to solve the image generation problem. **Overall recommendation:** -1 (weak reject) ### Detailed comments to authors The authors provide a novel idea of combining the contrastive learning and negative sampling methods. Please refer to the following for detailed evaluation. Advantages: 1. The idea of combining the contrastive learning and negative sampling methods is novel. 2. The authors provide the code and data. 3. The experiments are adequate. The only regret is that the authors do not replace their contrastive learning and negative sampling methods with other existing methods to verify the validity of their proposed methods. Concerns: 1. The authors claim that the combination of contrastive learning and negative sampling is novel. However, SGL and SimGCL mentioned in the paper also combine the two views. 2. The main modules of the model including LightGCN and SGM are proposed by existing work. 3. The methods for contrastive learning and negative sampling may be novel. However, the authors fail to articulate the advantages of their methods over existing methods. 4. Although the problem in recommendation systems is well known, the author should give the definition of the problem in the paper. 5. There are many inconsistencies in the paper, and the authors need to revise it carefully to achieve rigor. For example, 1) Table 4 claims that SGL and SimGCL belong to the methods using contrastive learning. However, Table 2 claims that the two methods belong to the methods using contrastive views and negative samples. 2) Table 1 claim that SGM belongs to the domain of recommendation, but SGM is proposed to solve the image generation problem. ## Reviewer 3 **Relevance to SIGIR:** 4 (good) **Originality of the work:** 4 (good) ### Originality of the work (comments) This work proposes a novel recommendation method that leverages diffusion model to generate negative samples and contrastive views. **Technical soundness:** 2 (poor) **Technical soundness (comments):** This work is not well-motivated. The experimental design could be enhanced. **Quality of presentation:** 4 (good) **Related work:** 2 (poor) **Perceived significance or potential impact of the contribution:** 4 (good) ### Strengths 1. This work studies on an interesting problem. 2. This work proposes a novel recommendation method that leverages diffusion model to generate negative samples and contrastive views. 3. The paper is well-writing. ### Weaknesses 1. This work is not well-motivated. 2. The experimental design could be enhanced. 3. This work misses a significant amount of relevant literature. **Overall recommendation:** -1 (weak reject) ### Detailed comments to authors While this work studies an important problem and is potential, it has some limitations: 1. My major concern lies in the motivation behind the proposed method. The authors propose the use of a diffusion model for negative sampling and contrastive learning, yet the rationale behind this choice remains vague. What specific advantages does the diffusion model offer for these applications? A more thorough analysis is needed to support their approach. Specifically, in the discussion on contrastive learning, the paper focuses solely on GSL and SimGCL, overlooking various advanced strategies such as [2][42][a1]. The limitations of existing methods and the benefits of the proposed approach are not adequately explained. Moreover, employing a diffusion model for data augmentation is not novel, as evidenced by [a5]. For negative sampling, the superiority of the diffusion approach over other strategies is not convincingly demonstrated. The arguement that covering the entire embedding space would yield better performance is questionable. If it were true, it means that randomly sampling over the entire space would be a best choice. So, why would diffusion be more effective than DNS and MixGCF that aim to sample hard negative negatives to accelerate learning? Besides, The paper also fails to consider other important and advanced sampling strategies like [a2][a3]. 2. The experiments are insufficient: 2.1 The compared methods are outdated. More SOTA methods on contrastive learning and negative sampling should be considered, like [2][42][a1][a2][a3]. 2.2 Given that the proposed method integrates contrastive learning with negative sampling, it is crucial to compare it against a simple combination of existing contrastive learning and negative sampling methods (e.g., SimGCL+MixGCF). Such a comparison would provide stronger evidence of the proposed diffusion-based strategy's effectiveness. 2.3 In the ablation study, SCONE-W/o NS should be benchmarked against existing contrastive-learning-based methods. Similarly, SCONE-w/o CL should be compared with existing negative samplers. Only through such comparisons can the effectiveness of the proposed method be attributed to the novel diffusion model rather than the combined application of contrastive learning and negative sampling. Unfortunately, from figure 7 and table 4, it seems that SCONE-w/o NS underperforms compared to SimGCL, casting doubt on the diffusion-based strategy's efficacy. 2.4 The authors assert that their method can generate diverse recommendations, yet they only evaluate the uniformity of the embeddings and not the actual diversity of the recommendation results. 3. This work misses some important related work. A few are listed here: [a1] WWW'23: Automated Self-Supervised Learning for Recommendation [a2] CIKM'23: Batch-Mix Negative Sampling for Learning Recommendation Retrievers [a3] WWW'22: Learning Recommenders for Implicit Feedback with Importance Resampling [a4]NIPS'24: Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion [a5] CIKM'23: Diffusion augmentation for sequential recommendation Some minor ones: 4. It would be better to envision some future directions. 5.It would be better to include a section of related work that comprehensive reviews recent developments in recommendation samplers, contrastive-based recommendation methods, and diffusion-based recommendation methods (like [a4][a5]). In summary, while the application of a diffusion model in negative sampling and contrastive learning is promising, the current work has some serious limitations in terms of motivation, experiments and related work. Consequently, I suggest a reject. ## Reviewer 4 **Relevance to SIGIR:** 5 (excellent) **Originality of the work:** 4 (good) ### Originality of the work (comments) The paper introduces a novel approach to generative views for contrastive learning and sample hard negative samples via score-based generative models. **Technical soundness:** 3 (fair) ### Technical soundness (comments) The method is overall sound. Experiments are conducted on six public datasets, which is good. However, there are issues in the evaluation: (1) In section 5.1.3, it seems that most hyperparameters for baseline methods are fixed. It's unknown whether the baselines have been well tuned for fair comparison. (2)Only@20 metrics are reported. Author(s) are suggested to report more metrics (e.g., @5, @10, or @50) to make the evaluation comprehensive. (3) 1 cannot draw the observation about "SCONE works better for cold-start users" from Figure 4. The improvements are relatively similar across different user groups. **Quality of presentation:** 3 (fair) **Related work:** 4 (good) **Perceived significance or potential impact of the contribution:** 4 (good) ### Strengths 1. This paper studies an important problem, i.e., contrastive learning for collaborative filtering models. 2. The idea of "generating views via leveraging the reverse process of score-based generative models" is interesting. 3. Experiments are conducted on six public datasets. 4. Code is available during the reviewing phase, which is good for reproduction. ### Weaknesses 1. Motivation is unclear. The idea and the proposed view generation method are interesting and novel. However, it's unclear why we need such method or what challenges does this method solve. In the introduction, the author(s) claim that a motivation is about thinking "can we integrate two approaches into a single unified framework?". Yes it's great that SCONE can do that, but is there any additional benefit compared to simply combining a contrastive learning method (like SGL) with DNS? 2. Statements are not well supported by experiments. 2-1. "Good performance", the proposed method uses both CL and hard negative sampling. However, all the baselines meatares baceaaortanteebaraanaae cesalanea canar caneponesone ese mot arnul totaocooiecasellne use either CL or hard negative sampling, resulting in unfair comparison. It's not painful to simply combine GCL methods like SGL or SimGCL with DNS. 2-2. "Sparsity", I cannot draw the observation about "SCONE works better for cold-start users" from Figure 4. The improvements are relatively similar across different user groups. 2-3. "Diversity", it's not so clear why higher uniformity leads to more diverse recommendation results. More evaluations (e.g., directly reporting diversity metrics) are required. 3. Evaluation issues. soms at ms bupararanotar for pacline merhade are fyed des urknou uhasher ahe 3-1. In section 5.1.3, it seems that most hyperparameters for baseline methods are fixed. It's unknown whether the baselines have been well tuned for fair comparison. 3-2. Only @20 metrics are reported. Author(s) are suggested to report more metrics (e.g., @5, @10, or @50) to make the evaluation comprehensive. **Overall recommendation:** -1 (weak reject) **Detailed comments to authors:** Please kindly refer to "Strengths" and "Weaknesses". ## Meta-review ### Meta-review This paper proposes a stochastic sampling framework which combines contrastive views and hard negative sampling, and has been evaluated on six benchmark datasets. However, the reviewers think the motivation for approach design is not clear (Reviwer 1 & 3 & 4) and also question about the novelty of the paper (Reviwer 2). There are also issues in the experimental design that needs further improvements (Reviewer 3&4). In sum, we hope the authors could polish the paper according to the review comments and resubmit it for future publication.