## Reviewer 1 **Overall recommendation:** 1 (weak accept) **Relevance to CIKM:** 5 (excellent) **Originality of the Work:** 4 (good) **Technical Soundness:** 4 (good) **Quality of Presentation:** 4 (good) **Impact of Ideas or Results:** 4 (good) **Reproducibility of Methods:** 4 (good) ### Detailed Comments to the Author(s) Strengths: 1. The idea of using generative models to solve the problems of views generation in contrastive learning and hard negative sampling is very innovative, bringing new insights and considerations to the recommendation systems community. 2. The paper is well-organized, with a clear structure and easily understandable content. 3. The authors conducted thorough experiments, demonstrating improvements across six benchmark datasets. Additionally, they analyzed the uniformity of the representations, showing that the proposed method not only mitigates popularity bias but also enhances the personalization of recommendations. Weaknesses: 1. Although the authors conducted experiments on multiple benchmark datasets, the performance improvements in Recall@20 and NDCG@20 are limited. 2. The selection of baseline methods for negative sampling does not include the most advanced negative sampling methods, such as DINS[1] and DENS[2]. Would the recommendation performance be better if the positive injection method of DINS were used? [1] Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, and Philip S. Yu. 2023. Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering. CIKM '23 [2] Riwei Lai, Li Chen, Yuhan Zhao, Rui Chen, and Qilong Han. 2023. Disentangled Negative Sampling for Collaborative Filtering. WSDM '23 ### Summary to support your recommendation The authors observe a similarity between different views in contrastive learning and the sampling of hard negative examples. To address this, they introduce generative models into recommendation systems to generate different views for contrastive learning, while also generating hard negative samples based on specific user-item interaction. This is a novel approach, and the idea is innovative, providing some inspiration for the development of the recommendation systems community. ## Reviewer 2 **Overall recommendation:** 0 (borderline paper) **Relevance to CIKM:** 5 (excellent) **Originality of the Work:** 5 (excellent) **Technical Soundness:** 5 (excellent) **Quality of Presentation:** 5 (excellent) **Impact of Ideas or Results:** 5 (excellent) **Reproducibility of Methods:** 5 (excellent) ### Detailed Comments to the Author(s) This paper proposes to integrate contrastive learning and hard negative samples into a single unified framework. The topic is interesting and the paper is well-written. However, the improvements are knd of limited even and no significant test results are provided. The paper doesn't compare their method to SGM-based recommendation methods. **Summary to support your recommendation:** Please refer to Detailed Comments. ## Reviewer 3 **Overall recommendation:** 0 (borderline paper) **Relevance to CIKM:** 4 (good) **Originality of the Work:** 4 (good) **Technical Soundness:** 4 (good) **Quality of Presentation:** 4 (good) **Impact of Ideas or Results:** 3 (fair) **Reproducibility of Methods:** 4 (good) ### Detailed Comments to the Author(s) Summary: This paper presents SCONE, a novel approach for generating contrastive views and hard negative samples in GNN based CF methods using score-based generative models (SGMs). SCONE uses a LightGCN model as the base recommender system and incorporates an SGM to generate contrastive views for self-supervised learning and hard negative samples for improved training using a co-trained SGM model on the node embeddings. The SGM is trained on the final embeddings from LightGCN and uses a stochastic sampling process to generate personalized views/samples for each user-item pair. For contrastive learning, two views are generated: a perturbed embedding and a generated embedding from the perturbed one. Hard negative samples are created by injecting positive sample information during the SGM sampling process making it harder than a randomly sampled negative. Experiments on 6 benchmark datasets show SCONE outperforms state-of-the-art baselines in most cases, especially on sparse datasets. Strengths: * The paper is well presented and easy to read and follow. * Experiments are extensive with in-depth ablations to understand the impact of different components and different hyperparameters. Includes runtime comparisons to address computational overhead concerns. * Novel application of SGMs to recommender systems for both contrastive learning and negative sampling. Weaknesses: * Since the method does both InfoNCE and BPR, no baseline with combination of these methods is presented. In other words, no ablation is shown to show the value of using the generative model for selecting the negatives. * Considerably slower than GCL but does not provide much benefits over that. Fig 5: no discussion on worse perf on high degree items than MixGCF: the claim is that SCONE provides both hard negatives and CV but the behavior is very similar to CV only. * Only LightGCN as backbone * No analysis of the generative model is included, how good is that model trained at various locations in training and how does it impact the model training * Fig4 there is not clarity that SCONE does better than GCL in sparse users ### Summary to support your recommendation Overall, SCONE presents an innovative and effective approach for improving recommender systems through generative modeling. But the current experiments and results do not effectively demonstrate the effectiveness of the proposed method. I believe more extensive experiments are needed to demonstrate the effectiveness of the proposed approach. Even with the limitations, I think the idea is interesting and worth discussion. ## Meta-review ### Meta-review The paper brings forward an innovative approach by applying SGMs to the recommendation systems domain, providing fresh insights into contrastive learning and negative sampling. Most reviewers find merits in the paper, including the well developed structure, the experimental setup, and the novelty of the method. However, they have also identified several problems including the limited performance (Reviewer 1), baselines (Reviewer 2 and 3), efficiency (Reviewer 3). Overall we recommend a reject, and the authors are encouraged to address the above issues in future submissions.