Title: Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Recommender Systems

Graph Contrastive Learning

Negative Feedback
Abstract: Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity. Our source code and data are released at \url{https://github.com/LQgdwind/nips25-sdcgcl}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents SDCGCL, short for Signed Dual-Channel Graph Contrastive Learning, a novel graph-based approach for recommendation which adopts contrastive learning and works on both positive and negative feedback of users. Specifically, the model builds upon a known limitation in the current literature, where almost no recommendation model leverages negative feedback to exploit the full spectrum of users' preferences over items. \n\nThe proposed framework involves the following steps. To begin with, two parallel graph neural networks are used to encode users and items embeddings for positive and negative feedback graphs, where an additional views of the embeddings are obtained in each considered graph at a specific propagation layer through an augmentation strategy on the node embeddings. \n\nSecond, a contrastive learning strategy is adopted to capture similarities between the constructed views within the same feedback graph (i.e., positive and negative), named as intra-channel contrastive learning. At the same time, an inter-channel distribution alignment mechanism is implied to avoid an excessive divergence between the learned positive and negative embeddings, as the two graphs (for the different feedback) can largely be largely divergent in their distribution. Finally, the two learned embeddings (positive and negative) are used to predict the user-item interaction with different importance weight $k$, a hyper-parameter of the model. \n\nIn addition to the above, the authors specify that the overall training consists of two separate phases. First, they implement a full-graph learning strategy, where both positive and negative graphs are exploited in their integrity to allow the model to learn global perspectives on the whole dataset. Soon after that, the authors propose to perform a random-walk sampling on the negative graph (based on items' popularity) to ease the computational complexity to work on the full negative feedback graph, which is (intuitively) way larger than the positive feedback one. The overall loss function is a multi-objective combination of different losses coming from the previous modules of the framework.\n\nEmpirical results on four popular recommendation datasets and against a very large plethora of recommender systems spanning unsigned and signed recommendation demonstrate the superior performance provided by the proposed approach. An ablation study further justifies the architectural choices made by the authors, where the fusion module appears to be the most fundamental one among all the proposed solutions. Additional analyses on the efficiency, robustness, parameter sensitivity, and a case study on distribution alignment, complement the evaluation of the model, which seem to work pretty well under many different perspectives.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n$\\bullet$ The paper tackles an understudied but quite important aspect in recommendation, namely, the usage of explicitly negative feedback. Very few papers in the literature are addressing this topic, and this is also evidenced by the lack of many datasets explicitly incorporating this information.\n\n$\\bullet$ The related work is detailed, and helps placing the proposed approach quite well in the current literature, outlining its positive aspects with respect to existing solutions.\n\n\n$\\bullet$ The methodology seems adequately sound, and all proposed solutions are always supported by theoretical proofs (provided in the Supplementary Materials). I particularly appreciated the calibration strategy between negative and positive feedback embeddings, as it is quite clear that the latter would largely diverge over the former without a tailored solution to address this problem. I also really liked the two stage optimization which allows to train the model on the global and local views of the graphs.\n\n$\\bullet$ The experimental setting is very extensive, with many baselines spanning signed and unsigned recommender systems, and several evaluation dimensions which help providing a large set of perspectives on the model's performance and architectural choices. Moreover, code is released at review time to ensure the full reproducibility of the work.\n\n**Weaknesses**\n\n$\\bullet$ Even acknowledging the page limitations, I think the paper would have benefitted from having a starting (short) section on the background and notations. Indeed, starting off with embedding and graph notations, considering that not all the readers might be fully expert on the topic, could represent a slight challenge for the average reader. I would suggest the authors add a background/preliminaries section at least in the Supplementary Material.\n\n$\\bullet$ Regarding the data augmentation function in equation (3), I am wondering why the authors decided to apply it to the embeddings instead of, say, to the adjacency matrix. Did they decide to perform an embedding dropout strategy instead of more traditional edge or node dropout? And, if so, why did they do that? I think this aspect should be better clarified in the methodology. \n\n$\\bullet$ Regarding the sampled-graph learning stage, I am wondering if choosing a random-walk strategy based upon items' popularity might impact on the diversity of recommendations. In other words, do the authors think that sampling the graphs based upon the items' popularity might enhance the visualization of popular items at the detriment of the niche ones? I think this aspect should be further clarified by the authors, maybe also by measuring additional recommendation metrics which account for popularity bias and diversity in the recommendation lists.\n\n$\\bullet$ Even acknowledging the pages limitations and formatting, I think it would have been beneficial to have Table 1 in the following page with respect to where it is right now, just for the sake of easy readability along with the respective section in the text.\n\nQUESTIONS:\nMy questions are mainly referred to some of the weaknesses I raised above.\n\nQ1) Regarding the data augmentation function in equation (3), why did the authors decide to apply it to the embeddings instead of, say, to the adjacency matrix. Did they decide to perform an embedding dropout strategy instead of more traditional edge or node dropout? And, if so, why did they do that?\n\nQ2) Regarding the sampled-graph learning stage, could choosing a random-walk strategy based upon items' popularity impact on the diversity of recommendations? In other words, do the authors think that sampling the graphs based upon the items' popularity might enhance the visualization of popular items at the detriment of the niche ones? For instance, I would suggest the authors perform additional evaluation on metrics such as the average percentage of long-tail items (APLT [\\*]) for the popularity bias and the expected free discovery (EFD [\\*\\*]) for recommendation diversity.  \n\n**Suggestions**\n\nI would further suggest the authors take a look at this quite novel recommendation dataset [\\*\\*\\*] with incorporates actual negative feedback from users. I think it could be very useful to further analyze the goodness of their methodology. \n\n**References**\n\n[\\*] Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling Popularity Bias in Learning-to-Rank Recommendation. In RecSys. ACM, 42–46.\n\n[\\*\\*] S. Vargas, P. Castells, Rank and relevance in novelty and diversity metrics for recommender systems, in: RecSys, ACM, 2011, pp. 109–116.\n\n[\\*\\*\\*] Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, and Tat-Seng Chua. 2022. KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. In Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management (CIKM '22). Association for Computing Machinery, New York, NY, USA, 540–550. https://doi.org/10.1145/3511808.3557220\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the SDCGCL framework for effectively utilizing negative feedback in recommender systems. The framework includes three key components: Dual-Channel Graph Embedding, Cross-Channel Distribution Calibration, and an Adaptive Prediction Strategy. Based on this framework, the authors further propose the DualFuse model and develop a two-stage optimization strategy to ensure efficient training. The authors have made all their research open-source. Theoretical analysis indicates that the framework addresses the challenges of existing methods in terms of model compatibility, information exchange, and computational efficiency. Experimental results on four public datasets validate its effectiveness, showing that the proposed method significantly outperforms state-of-the-art baselines on multiple metrics while having the lowest computational complexity.\n\nSTRENGTHS AND WEAKNESSES:\nThe proposed SDCGCL framework is novel in its design, can be seamlessly integrated with existing graph contrastive learning methods, and effectively utilizes negative feedback to enhance recommendation performance.\n\nThe theoretical analysis is in-depth. Through theorems, it proves the instability of the negative feedback distribution and the role of the cross-channel fusion mechanism in information preservation, providing a solid theoretical foundation for the framework's effectiveness.\n\nThe experimental design is comprehensive. Comparative results on multiple datasets show that the method significantly outperforms existing baseline methods on metrics such as Recall and NDCG, and also has higher computational efficiency.\n\nRegarding the choice of the sampling rate ρ in the two-stage optimization strategy, although the paper mentions that ρ=0.01 yields the best results, it does not detail whether the adaptability of this parameter differs across various datasets.\n\nThe DualFuse Model is missing an overall flowchart.\n\nQUESTIONS:\nIn Figure 2, the \"Negative Graph Sampling Pool\" section lacks a corresponding explanation.\n\nWhy does Equation 6 ensure that $\\delta_1 \\to 0$，$\\delta_2 \\to 1$？ A proof is missing.\n\nEquation 7 seems problematic. It appears to subtract the negative feedback signal from the positive feedback, rather than aggregating the two feedbacks. It feels like it should be  $\\hat{y}_{u,i}=(1-k)\\mathbf{E}_{u}^{+}\\circ\\mathbf{E}_{i}^{+\\intercal}+k\\mathbf{E}_{u}^{-}\\circ\\mathbf{E}_{i}^{-\\intercal},$ . Furthermore, based on the formula, it seems the authors only use a portion of the negative feedback (since k can be 0). If so, this does not address the second challenge, which is described in the paper as: \"existing methods mainly focus on positive feedback and only partially utilize negative feedback, leading to an incomplete understanding of user preferences and suboptimal recommendations.\" right?\n\nLIMITATIONS:\nYes, the authors explicitly discuss the limitations of their work in Appendices F and G (e.g., industrial deployment, dynamic feedback, cold-start problems), state that the research has no potential negative social impact, and the explanation is sufficient and conforms to academic standards.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses challenges in leveraging negative feedback to enhance recommendation performance, specifically focusing on limitations in model compatibility, ineffective information exchange, and computational inefficiency. The proposed SDCGCL is a model-agnostic framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework comprises three key components: a graph embedding module for processing positive and negative graphs, a calibration mechanism for graph structural alignment, and an adaptive prediction strategy for effectively fusing information from both channels. The effectiveness of the proposed method is validated through extensive experiments on four public datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n1.The paper proposed a clear and clean architecture design, each module serves a distinct and well-motivated purpose.\n2.The experimental baselines are comprehensive and up-to-date. The embedding visualizations are useful for understanding the impact of each module.\n3.The proposed model-agnostic framework increases its applicability to various GNN-based backbones. In addition, the introduction of the DualFuse model further strengthens the cross-channel fusion capability.\n\nWeaknesses\n\n1.The model introduces several important hyper-parameters (such as $\\k, \\lambda, \\gamma$). Although the paper includes a sensitivity analysis, the number and significance of these parameters may increase the tuning burden in real-world deployments.\n2.When visualizing the embeddings in Figure 4 and the figures in Appendix, a hollow region consistently appears, surrounded predominantly by blue points (representing negative users) and exhibiting a distinct crescent shape. This phenomenon persists across different datasets and under various ablation settings, e.g., with different modules removed. Is there any specific design in the model causing this result? What is the impact on the final performance.\n3.The size of font in Figure 5 is too small, which affects the readability.\n\nQUESTIONS:\nPlease refer to the concerns raised in the Weaknesses section.\n\nLIMITATIONS:\nPlease see the Weaknesses section", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SDCGCL (Signed Dual-Channel Graph Contrastive Learning), a model-agnostic framework that effectively leverages both positive and negative feedback in recommender systems through dual-channel graph embedding, cross-channel distribution calibration, and adaptive prediction strategie\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1.  Can be seamlessly integrated with existing graph contrastive learning methods (SGL, LightGCL, XSimGCL)\n\n\n Weaknesses:\n\n1.  Model requires clear positive/negative ratings, may not work well with implicit feedback systems. Many modern recommendation systems (e.g., YouTube, TikTok, Spotify) primarily collect implicit feedback such as clicks, views, time spent, scroll behavior, or skip actions, rather than explicit ratings\n\n\n2.  While the paper provides formal theoretical foundations, several aspects could be strengthened. Theorem 1 assumes specific variance relationships (variance ≥ 1) between positive and negative feedback distributions, but real-world data may not always satisfy these assumptions, especially across different domains or user populations. Is it reasonable? While the popularity-guided random walk sampling is empirically effective, the theoretical analysis doesn't rigorously prove why this specific sampling strategy is optimal compared to other graph sampling approaches\n\n\n3.  Most recent state-of-the-art methods in REC graph learning and contrastive learning approaches are missing, potentially overstating relative performance (e.g. [1][2] )\n\n\n[1] Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective NeruIPS 2023 Zhang et al.\n\n\n[2] How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective WSDM 2025 Lin et al.\n\n4.  The binary rating threshold (≥4 positive, <4 negative) creates artificial negative signals that may not reflect genuine user dislike, potentially inflating the apparent effectiveness of negative feedback utilization\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nSee weakness.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}