## Reviewer 1 ### Summary and contributions Graph-based Collaborative Filtering (CF) is used in recommendation systems but faces issues with data sparsity and negative sampling. This paper introduces a new method, SCONE, which uses stochastic sampling for contrastive views and hard negative samples to address these challenges. This sampling method is built on score-based generative models and produces varied views and hard-to-find negative samples. The experiments on 6 datasets demonstrate the accuracy and robustness of the proposed method. ### Strong points (1) This work generalizes the contrastive learning (CL) view and negative sampling (NS) in the CF model into a sampling problem, which is a novel idea. (2) This paper introduces score-based generative models (SGMs) into the CF, which is a relatively new idea. (3) The work analyzes and compares the existing issues of existing CL and NS methods, which is very clear. ### Weak points (1) In the introduction section, the description of the method should give a high-level overview of the motivation and emphasize why it can solve the issues faced by CL and NS, and should not write the method in too much detail. For example, the author spends a lot of paragraphs introducing specific details of the method in lines 164-193, which is relatively redundant. (2) The limitations of the CL-based and NS-based method are repeatedly emphasized in the paper. CL: Perturbing the structure of the graph may cause the potential loss of interaction or topological information. NS: Synthetic negative samples are usually in a predetermined region, which may lead to limited diversity. The work presented in this paper does not highlight why both limitations can be addressed simultaneously. (3) some related works on CL and NS in CF are missing: - On the Theories Behind Hard Negative Sampling for Recommendation. WWW’2023 - Adap-: Adaptively Modulating Embedding Magnitude for Recommendation. WWW’2023 - Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering. NeurIPS’2022 (4) Figure 9 shows that the proposed method is very sensitive to the weight w, so it is difficult to adjust the value range of w in practice. (5) The paper does not provide a discussion of the complexity analysis or model size. ### Detailed evaluation Although introducing SGMs into CL and NS-base CF models is a relatively new idea, this paper should focus on explaining why introducing SGMs can effectively solve the limitations of existing methods. For example, the author should give some theoretical or experimental explanations. **Overall rating:** Weak Accept (Probable accept, unless convinced otherwise) **Reviewer confidence:** Knowledgeable: I have read papers on this topic. ## Reviewer 2 ### Summary and contributions The paper introduces a duffision method to generate the hard negative samples for collaborative filtering. ### Strong points 1. The paper is well written, and it provides very details about the negative sampling in the collaborative. 2. The authors use diffusion algorithm gor negative sampling, which is also interesting. I haven't seen similar work in the area of collaborative filtering in my mind. 3. The authors provide a detailed experiments to validate the proposed algorithm. ### Weak points 1. The novelty of this work is not high. 2. The complexity is not discussed, and the improvement of this work is mild. 3. Some simple baselines are not included. ### Detailed evaluation In this paper, the auhtors introduced SCONE, a novel approach to address the challenges of data sparsity and negative sampling in graph-based CF models. The SCONE proposes the concept of synthesizing samples, which had not been explored before in recommender systems. By employing a stochastic sampling process, SCONE generates user-item-specific augmented views for contrastive learning. Here are my concerns: 1. The novelty of this work is not high. The paper is a mixture of Graph Neural network, diffusion, and contrastive learning. Its mainly adopt the generative algorithm to generate the negative samples. The novelty is limited. 2. The complexity is not discussed, and the improvement of this work is mild. 1) Compare to the mixup techniques like MixGCF, I think the entire process in this work is expensive. The authors may need to provide complexity analysis to make the algorithm more convincing. 2) The improvement of this work is mild. As shown in table 2, the relative improvement is < 2% in all dataset, except douban (2.79%). It may raise a question that what is the main benefit of use complex diffusion algorithm to generate negative samples. 3. Some simple baselines are not included. Some related work but much cheaper and simpler algorithms are not compare in the experiment. For example: Disentangled Negative Sampling for Collaborative Filtering, WSDM 2023 Overall, the duffusion algorithm for negative sampling is interested. However, its performance and complexity may question the reader that what is the benefits of adopting the proposed work. **Overall rating:** Reject (Will argue strongly to reject) **Reviewer confidence:** Knowledgeable: I have read papers on this topic. ## Reviewer 4 ### Summary and contributions The paper proposes to use a generative model to find contrastive views that will be used to train a collaborative filtering model based on LightGCN with a contrastive loss. The diffusion model is applied on the final embeddings E learned by LightGCN. ### Strong points The idea of using diffusion to create contrastive views seems interesting. However my experience is that diffusion models do not work very well on recommendations so the samples obtained may not be very good. This was not discussed in the paper though. The paper reports the optimal values for a selection of hyperparameters on each dataset as well as two search spaces. There is source code. The analysis looks for the model robustness at different sparsity levels and includes ablation studies, hyperparamete sensitivity and efficiency. ### Weak points I don't think the paper explains the intuition behind the "positive injection". Indeed to me seems a contradiction to find a hard negative by adding positive embeddings. I understand the goal is to be at the boundary between positive and negative, but still I am missing why this should be a good idea. The fact that the results show it works is not enough, because there are so many other factors that can affect the results that are completely uncorrelated to how good the model is (for example inconsistent basleline optimization). - "To make a fair comparison with previous studies, we follow hyperparameters settings in the original papers." This is an example of bad practice that will result in an inconsistently optimized set of baselines which, as several reproducibility papers have shown, essentially invalidates any result reported in the paper. Indeed the reported baselines did not use the same train-test split and datasets. It is unfortunate because now we cannot derive any conclusion from the experimental section. ### Detailed evaluation The introduction section is very long and covers details that are usually left for the "related works" section. I suggest the authors consider to simplify the introduction focusing on a high level motivation and description of the contribution, moving the details in section 2. "It is not feasible to directly apply the denoising process of the diffusion model, which generates a new sample x̂(0) from a noisy sample x(T), to contrastive views generation, because it cannot maintain the specific characteristics of users and items." The statement is not justified and should be better explained. **Overall rating:** Reject (Will argue strongly to reject) **Reviewer confidence:** Expert: I have published papers on this topic. ## Reviewer 5 ### Summary and contributions This paper proposes a novel approach called SCONE (Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering) to overcome challenges faced by graph-based collaborative filtering (CF) models, resulting in significant improvements in recommendation accuracy and robustness. The paper introduces a score-based generative model that leverages contrastive learning and negative sampling to learn user and item embeddings. The proposed approach also addresses user sparsity and item popularity issues through user-item specific stochastic sampling. The paper provides extensive experimental results on benchmark datasets, demonstrating the superiority of SCONE over various state-of-the-art models. The contributions of this paper include a novel approach to graph-based CF, a comprehensive evaluation of the proposed approach, and significant improvements in recommendation accuracy and robustness. ### Strong points 1) SCONE's Novelty: The paper introduces SCONE, which combines various techniques to tackle CF challenges, notably addressing user sparsity and item popularity. 2) Evidence: Through experiments on six datasets, SCONE consistently outperforms top models, highlighting its effectiveness. 3) Clarity: The paper details SCONE's components and experimental methods for easy comprehension and replication. ### Weak points 1) Despite extensive testing on six datasets, the paper doesn't compare its approach to state-of-the-art models like NCF and DeepFM. 2) The paper touches on privacy and bias in recommendation systems but lacks a thorough discussion on their treatment in the proposed method. 3) While scalability of the approach is mentioned, there's no empirical evidence provided to back this claim. 4) The paper details results but omits specifics on the experimental setup, such as hyperparameter selection and data preprocessing. ### Detailed evaluation Overall, the paper presents some contributions to the field of CF by proposing a novel approach that addresses prevalent problems in CF and provides significant improvements in recommendation accuracy and robustness. The paper provides clear and detailed explanations of the proposed approach and extensive experimental results that demonstrate its effectiveness and robustness. However, the paper could be improved by comparing the proposed approach with some state-of-the-art models, providing a more in-depth discussion on privacy and information bias issues, providing a detailed analysis of the scalability of the proposed approach, and providing a more detailed explanation of the experimental setup. **Overall rating:** Weak Reject (Probable reject, unless convinced otherwise) **Reviewer confidence:** Knowledgeable: I have read papers on this topic. ## Meta-review ### Summary The paper presents SCONE, a new approach designed to overcome data sparsity issue faced by graph-based collaborative filtering (CF) models. SCONE leverages a diffusion model to generate hard-to-find negative examples for contrastive learning and negative sampling to enhance the learning of user and item embeddings. While the proposed method is reasonable, reviewers have raised concerns about its novelty and the justification of the motivation. A more significant issue is the rigor of the experiment. Given the extensive literature on recommender systems, it is critical to include a comprehensive set of baselines into *fair* comparison and conduct a thorough analysis of the results to support the claimed contribution. We consider the paper not ready for publication before an improved experimental study is conducted. **Recommendation:** Reject