Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
Authors: Leqi Zheng
Chaokun Wang
Zixin Song
Cheng Wu
Shannan Yan
Jiajun Zhang
Ziyang Liu
Venue: NeurIPS 2025 poster ()
Keywords: Recommender Systems
Graph Contrastive Learning
Negative Feedback
Primary area: other

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}.

Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
Leqi Zheng' ', Chaokun Wang 1. Zixin Song' ' Cheng Wu 1
Shannan Yan 1, Jiajun Zhang? 2, Ziyang Liu
TTinggua University ?University of Science and Technology of China (zhenglq24, songzx24, wuc22, ysn24, liu-zy21 | @ maiil.tsinghua.edee chaokun @ tsinghua. edu.en, zhangjiajunS| @emiil.usc.co..
Abstract
Traditional recommender systems have relied heavily on positive feedback for learn- ing preferences, while the abundance of negative feedback in real-world scenar- ios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems 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-Chinn Tual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (I) Dual-Chame Graph Embed- ding that separately processes positive and negative graphs, (2)
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 two-stage optimization strategy ensure efficient training. Extensive experiments on four public datasets demonstrate that our ap- proach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity. Our source code and data are released at http://htw....ooooogg///////////
Introduction
Recommender systems have become integral components of modern digital platforms, significantly influencing user engagement and satisfaction across diverse domains such e-commerce, social media, and content streaming services. While substantial progress has been made in leveraging positive feedback (e.g.. likes, high ratings) for recommendation, the effective utilization of negative feedback (c.g., dislikes, low ratings) remains critical yet underexplored avenue for enhancing recommendation performance [2T 6]
This disparity is particularly noteworthy given that negative feedback often provides explicit sig- nals about users' preferences and can potentially offer more precise guidance for recommendation refinement than positive feedback alone [22 52 43). As shown in Figure [ traditional unsigned graphs only capture the existence of positive interactions, whereas sign-aware graphs preserve the polarity of feedback through different edge types, enabling more comprehensive modeling
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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of user preferences. Therefore, the recent research 34
29
6
43
has increasingly focused on
negative feedback in recommender systems. However. these studies face three major challenges: (1) Limited Model Compatibility: Most existing meth- ods design specialized models for processing signed feed- back [49 43,, making them incompatible with recent ad- vances in graph- recommendation models. This spe- cialization prevents them from benefiting from state-of-the- art techniques like graph contrastive learning, which have demonstrated remarkable success in unsigned recommen- dation scenarios. (2) Limited Information Exchange: Most existing methods treat negative feedback as auxiliary signals, failing to fully exploit its potential (34 66. These methods primarily focus positive feedhack while only partially utilizing negative feedback, resulting in an incom- plete understanding of user preferences and suboptimal recommendations, (3) Limited Training Strategy: Ex- isting methods either process the full signed graph during training or rely solely on sampled feedback [49 []. failing to strike a balance between comprehensive learning and training efficiency.
User
Iten
Rotings
3
5
Unsign gned
Use-IIen Interactione
Sign Aware Snoph
Figure Comparison hetween unsigned graph and sign-aware graph in recommender systems. Left: User-item interactions with explicit ratings showing both positive (4-5) and negative (1-3) feedback. Right: Un- like unsigned graphs (top). sign-aware graphs (bottom) preserve feedback polarity through different edge types.
Motivated by the aforementioned issues, propose novel Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that revolutionizes the integration of negative feedback recom- mendation systems. The framework consists of three key components: (1) Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency between channels, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. To further enhance the framework's 's effectiveness, implements dual -hannel graph encoder and cross- cross-channel graph fusion, enabling simultaneous processing of positive and negative feedback patterns. To address training efficiency, we also propose a two-stage optimization strategy that combines comprehensive learning on full graphs with effi- cient training on strategically sampled subgraphs. This approach theoretically proven preserve recommendation quality.
present the Dual-channel Feedback Fusion (DualFuse) model, which
The main contributions of this work summarized as follows:
Model-Agnostic Framework: We propose SDCGCL, a model-agnostic framework that can be seamlessly incorporated into existing graph contrastive learning methods, overcoming the compatibility limitation of current signed recommendation approaches (Section 2.1
Cross-Channnn Information Fusion: We design DualFuse, novel model featuring dual- channel encoding and cross-channel fusion mechanisms to enable effective information ex- change between positive and negative feedback patterns while preserving channel-specific characteristics (Section 2.2).
Two-Stage Training Strategy: We develop two-stage optimization strategy combining comprehensive learning on full graphs with efficient training on strategically sampled subgraphs, with theoretical guarantees for both training effectiveness and efficiency (Section B).
Experimental Validation: Extensive experiments four public datasets demonstrate that approach consistently outperforms the state-of-the-art baselines by substantial margins, while achieving the superior computational efficiency with faster convergence (Section 4).
2
Model Design
In this section, we propose two key novel techniques: (1) model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework (Section 2.T). and (2) a Dual-Channel Feed- back Fusion (DualFuse) model specifically designed to complement this framework (Section 2.2

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bual- Coli-Chhnnell Graph Embeddlng
Cross-Channel Channel Distribution Calibration
Intra-Chameel Contrastive Learning
Inter-Charnel Distribution Aligrment
Carvatti Luania
Hep Scors
P
Regetia oa
Original Vitw
(ferarsted 60 mode))
Original Vine
Negative Graph Sampling Pool
Adaptive Prediction Strategy
For wach spoce rondonly qp one grage reger yrogh.
C
ROOD
Figure 2: Overview of the SDCGCL. framework. The framework consists of three main components: (I) Dual-Channel Graph Embedding, (2) Cross-Channe Distribution Calibration, and (3) Adaptive Prediction Strategy. negative graph sampling pool (bottom left) enables efficient training optimization.
2.1 agnelccg SDCGCL Framework
In this subsection, we propose our model-agnostic Signed ing (SDCGCL) framework, which effectively leverages both positive and negative user feedback for recommendation tasks. The SDCGCL framework consists of three key components: dual-channel graph embedding, cross-channel distribution calibration, and an adaptive prediction strategy, as illustrated in Figure 2
Graph Contrastive Learn-
2.1.1 Dual-Channel Graph Embedding
To effectively leverage both positive and negative feedback, our SDCGCL framework independently propagates the positive and negative interaction graphs using graph contrastive learning techniques.
For each channel, independently encode the user and item embeddings through message propagation function f(-) and a dropout strategy function p(-), which can be instantiated with any suitable GNN backbone. The embeddings are updated over L layers:
1. = (p(pp(() et-1)
(p(A
= - (v(A ()
e,,
(1)
where -., e denote layer 1 embeddings, -, 0 represent layer | (-1 item embeddings, and A+ À are normalized adjacency matrices in positive/negative channels. After L layers of propagation, we obtain the final graph embeddings:
E = AGG (fet, :1 = E, = AGG (fe (fe_, :15
where Σ and E,, denote final graph embeddings in positive/negative channels, and AGG(.) denotes a function that aggregates embeddings from different layers, such as mean pooling or concatenation.
To generate contrastive views for contrastive learning, we apply the base model's augmentation mechanism to obtain augmented embeddings designated layer I":
z
" (A+ (t,,e,,,0)))) 0). "
A e X,
where ((-) represents the base model's specific augmentation mechanism, X denotes optional node features, and contains augmentation-speen specific parameters (e.g., dropout rates). The final contrastive embeddings then obtained by aggregating the augmented embeddings:
Z = AGG" (14t,-)), z = AGG (12,,--)
where AGG" (.) is the contrastive view aggregator. Similar notations apply to items with embeddings E E, Z and Z,

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2.1.2 Cross-Channel Distribution Calibration.
To effectively integrate information from both positive and negative feedback channels, our frame- work employs cross-channel distribution calibration mechanism, which achieves the information integration through two components: intra-channel contrastive learning and inter-channel distribution alignment.
Intra-Chine Channel Contrastive Learning, Given that user preferences exhibit inherent structure within both positive and negative interactions, we first employ channel-specific contrastive learning to capture these underlying patterns. For the positive channel, we optimize:
E
E
Z |
min - (/"(0//EE 3.) (u.))ET+
(E
Σ
}}
(5)
M
(a jeEg uf u ş
Here, the first terms l" /"E z+ and (E z maximize agreement between original em- beddings and their augmented views, encouraging robustness to perturbations. Similarly, for the negative channel:
[ Znd |4
(E 2p
min (E, (u
(6)
|
(u E9) s str,, 74
Inter-Channel Distribution Alignment While maintaining channel-specifie information is impor- tant, the excessive divergence between positive and negative embedding spaces can hinder effective integration. We propose inter-channel distribution alignment mechanism that enforces structural consistency while preserving distinctive features:
min Σ uELl
9
E o
E |
E o E
g iEI
E E ||
Σ (o.))
E, o E
}}
||NTT
(uJ)eS
(u.i)EG+
where g" (:.)) represents distribution difference measure between positive and negative channels. The first equation aligns user-centric patterns, while the second addresses item-centric alignments, with normalized interaction scores reflecting neighborhood aggregations.
2.1.3 Adaptive Prediction Strategy
After obtaining the calibrated embeddings from both channels, we adopt an adaptive prediction strategy combine them for final recommendation. The predicted preference score ÿa,; for user and item i is computed by balancing the contributions from the positive and negative embeddings:
iu.: = (1 + E) o E, - -KE a E,T,
where & [0, 1] is hyperparameter controlling the influence of negative feedback.
2.1.4 Theoretical Analysis
Theorem (Distribution Instability). For each node, the negative neighbors N have an unstable degree of scale. For users, some only give positive ratings and refrain from commenting itmss they dislike, while others are more direct and express their negative ratings openly. Therefore, in signed recommendation graphs, the negative feedback distribution exhibits higher variance than positive feedback, with embedding distributions satisfying:
E T - μ4 E E
P Var Et ETT]= E o = 8,0²
(9)
where 82 2 represents the inherent instability of negative feedback.

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Proof Sketch. We show that without distribution alignment, the prediction expectation contains bias term -ôš and inflated variance dependent on d2. Distribution alignment (Eq. 7 ensures d1 + 0, 82 > 1, normalizing the prediction expectation to R(ju.i = μ and simplifying variance to (2k + 1) o? The complete proof is provided in Appendix A.1
2.2 DualFuse Model
SDCGCL. is model-agnostic and can integrate with various graph contrastive learning models like SGL., XSimGCI, and LightGCI. [46 2] However, these models lack negative feedback utilization,, limiting their ability fully exploit the potential of our framework. To address this inadequate utilization, propose DualFuse, which implements dual-channel graph encoding and cross-channel fusion positive and negative graphs, enabling simultaneous learning of both interaction patterns to maximize SDCGCL's effectiveness.
2.2.1 Dual-Channel Graph Encoder
DuslFuse employs dual-channel graph encoder based on LightGCN, with distinct embedding spaces for each channel. Embeddings evolve through layer-wise message propagation within channels, and the final representations are computed via multi-hop connectivity aggregation:
El=o EieNt VIN. HA,
El EieN, VIN. HAG
E
E
(10)
L+1
L+1
where E and E, denote final graph embeddings in two channels. Similarly, for item i, we can obtain E, and E,
2.2.2
Graph Fusion
DualFuse leverages an innovative cross-channel graph fusion mechanism where embeddings from each channel create perturbations for the other, enriching representations while preserving channel- specific patterns, as shown Figure 3
At designated layer 1", we generate contrastive views by introducing structured perturbations derived from the opposite channel. This contrastive views implements the data augmentation function p-)) from Equation through cross-channel fusion:
Origial
Graph Fusion
Costrastve
DEL D
DIO DA0
L
DONO
DA00
z
+
Fullee
L+1
1
le,,--1'
1=0
(11) Figure Graph fusion mechanism: Original embeddings from positive (orange) and neg- ative (purple) channels are normalized and fused to generate contrastive views.
1
u.!-
L
cot |
1=0
where Z and Z denote final contrastive embeddings in two channels. Similarly, for item we can obtain Z and Z
2.2.3 Theoretical Analysis
Theorem 2 (Cross-Channed Information Preservation). The cross-channel fusion mechanism pre- serves essential information while maintaining stable gradient flow. For any node v E U U.I, I, at convergence:
|
Cll
~
|Vo=fll Cll
|
(12)
Proof Sketch. By analyzing gradient propagation through the fusion mechanism, we establish that convergence, when || oz = || 2c and ||ct| = ||c_ the gradients in both channels maintain similar magnitudes, ensuring balanced information flow. The detailed derivation is available in Appendix A.2
5

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Optimization Design
To effectively train our framework while managing computational complexity, we propose two-stage optimization strategy that combines comprehensive learning on the full graph with efficient training on strategically sampled subgraphs.
3.1 Two-Stage Optimization Strategy.
Full-Graph Learning Stage The first stage operates on the complete signed user-item interaction graph 9, processing all positive edges E+ and negative edges E simultaneously. The full-graph learning stage is crucial for capturing the complete structure of user preferences and ensuring that no valuable negative feedback information is overlooked during the initial training period.
Sampled-Graphe Learning Stage. To address the computational challenges posed by large-scale negative interaction graphs while maintaining learning effectiveness, we propose popularity-gun -uiided random walk sampling strategy that formally presented Algorithm [ This strategy carefully constructs subgraphs that preserve the most informative negative feedback patterns.
Algorithm 1: Popularity- Guided Random Walk Sampling
Input Original negative graph G sample rate p. walk length I, temperature 7
Output Sampled negative graph G
Initialize node degrees d, for all E V/;
Compute importance distribution P(n) +- Eecv exp(d./7))
exp(d=/7)
< Sample plV| nodes according to P(r);
Initialize importance scores 8u +- for all V: for each starting node 1o; for to do
do
day Compute transition probabilities P(uslue) + ENi dez
Sample v from ((ve) according to P(uylet); Update importance: Su, Su, du, t-t,
end end
Compute edge importance w.j + *xt: for each edge (i,j); Construct G with edges where Way > return ga
Theoretical Analysis. The effectiveness of our two-stage sampling strategy can be theoretically justified through embedding stability bounds:
Theorem 3 (Two-Stage Stability Bound), For any node EUII, the expected embedding difference satisfies:
l2=
[Cil (cal))/ ( +
t t T Twarm
E (()))
(13)
t> Twurm
where Oj == L2D integrates the initial learning rate (no), the Lipschitz constant (L) of loss gradients in dense interaction regions (lje,, eill2 2 ô), and the maximum node distance (D); 3. =dtx/e) combines gradient smoothness (L²), base variance (oī) from full-
Cz(p) = n [ [ graph training, and sparse sampling penalty (k/p²); e(p) = /(p) encodes information loss where v(p) measures divergence between true and sampled negative feedback distributions.
Proof Sketch. We analyze each optimization stage separately. In the warm-up phase, embedding differences decay ((1()). In the sampling phase, the decay follows O(1/l) - -O(1/VI), reflecting the trade-off between sampling efficiency (p) and variance control (x, v). The full derivation provided in Appendix A.3

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Table 1: The performance comparison the methods four datasets. Baseline best results in bold, SDCGCL best results in bolds, second-best underlined. Relative improvement (%) shows the performance gain of SDCGCL-Dual over the strongest baseline. indicates statistical significance (p < 0.01).
Group
Datasets
ML-IM
Yelp
Amazon
ML-10M
Models
Recall
NDCG
Recall
NDCG
Recall
NDCG
Recall
NDCG
MF
0.1329
0.1988
0.0334
0.0217
0.0489
0.0364
0.1667
0,2046
NCF
0.1501
0.2102
0.0359
0.0237
0.0578
0.0427
0.1926
0.2441
NGCF
0.1630
0.2185
0.0566
0.0475
0.0614
0,0463
0.2162
0.2718
LightGCN
0.1993
0.2632
0.0662
0.0539
0.0728
0.0631
0.2597
0,3091
DGCF
0.1768
0.2104
0.0629
0,0504
0.0695
0.0613
0.2241
0,2859
HyRec
0.1805
0.2181
0.1606
0.0550
0.0658
0.0596
0.2304
0.2805
U-RS
GFormer
0.2272
0.2407
0.0597
0,0542
0.0758
0.0672
0.2460
0,3011
SelIGNN
0,2565
0,2810
0.1791
0.0672
0.0806
0.0724
0.2742
0,3111
NCL
0.2627
0.2782
0.0699
0.0615
0.0746
0.0676
0.2972
0.3183
SGL
0.2798
0,3037
0.0746
0.0729
0.0958
0.0694
0.3056
0.3299
LightGCL
0,2730
0,3035
0.0697
0.0675
0.0967
0.0728
0.3098
0.323
XSimGCL
0,2729
0,3087
0.0867
0.0963
0.3109
IGCL
0.2747
0.3016
0.0692
0.0660
0.0796
0.0661
0.2956
0.3212
SiReN
0.3093
0.3338
0.0873
0.0635
0.1017
0.0924
0,3490
0.3583
SiGRec
0.1937
0,2583
0.0594
0.0499
0.0741
0.0678
0.2302
0,2918
DEGNN
0,2538
0,3030
0.0728
0.0609
0.0768
0.2721
0,3113
SignGI
0.1635
0.2225
0.1607
0.0536
0.0736
0.0644
0.2366
0,2970
S-RS
SBGNN
0.1527
0.2113
0.0621
0.0479
0.0612
0.0548
0.2237
0.2773
SLGNNN
0.1740
0,2370
0.0658
0.0498
0.0617
0.0556
0.2269
0,2912
SGFormer
0.1877
0.2680
0.0601
0,0459
0.0792
0.0648
0.2344
0,2908
SIGForeer
0.2995
0,3380
0.0856
0.0777
0.1006
0.0997
0.3217
0.3549
NFARec
0,2840
0.3212
0.0971
0.0808
0.1136
0,1020
0.3316
0.3442
SDCGCL-SGL
0,2879
0,3300
0.1136
0.0907
0,1108
0.1001
0.3768
0,3716
SDCGGL-LLggtGCL
0,2945
0.3423
0.1069
0.0826
0.1057
0.0890
0,3810
0,3760
Ours
SDCGCL-XSinGCL
0,3050
0,3401
0.1112
0.0881
0.1142
0,1014
0,3791
0.3726
SDCGGL--DalFuse
0.3282
0.3693
0.1243
0.0959
0.1342
0.1113
0.3900
0.3860
Relative improvement (%)
6.110%
9.260%
28.012%
18.689%
18.133%
9.118%
11.748%
7.731%
3.2
Multi-Objective Loss Integration
Our training approach integrates three key loss components to effectively capture both positive and negative feedback patterns: (1) recommendation loss based on Bayesian Personalized Ranking for supervision, (2) contrastive learning loss to enhance embedding quality within each channel, and (3) distribution alignment loss to maintain consistent structural information between channels. The final objective function combines these components with balanced weighting parameters:
L = Lree + X(Let + diee) + + 1
(14)
where controls the overall contribution of the auxiliary objectives, weights the distribution alignment constraint, and is the L2 regularization coefficient applied to model parameters θ A comprehensive description of each loss component and their mathematical formulations is provided in Appendix B
Experiments
4.1 Experimental Setup
We evaluate method on four publicly available recommendation datasets: Yelp, Amazon, and MovieLens (ML-IM and ML-10M). Following established conventions, we binarize ratings (scores 2 as positive, < 4 as negative). For the performance evaluation, adopt Recall 20 and NDCG@20 20 as metrics. We benchmark against 22 sfate-of-the-art recommendation methods across unsigned and sign-aware recommendation systems. Detailed descriptions of datasets, metrics, baselines, and parameter settings are provided in Appendix
4.2 Overall Performance
Experimental evaluations demonstrate our approach's superior performance. As shown in Table T DualFuse consistently outperforms all basclines by substantial margins. Compared to the best
7

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unsigned baseline (XSimGCL), our model achieves 20.26% and 19.63% improvements in Recall @ 20 and NDCG@20 on ML-IM. Against sign-aware methods, DualFuse shows significant gains over NFARec, with Recall @ 20 improvements of 6.11% (MI.-IM), 28.01 % (Yelp), and 18.13% (Amazon). SDCGCL, framework enhances all integrated methods (see Appendix (D.2), validating our dual- channel architecture's effectiveness.
4.3 Ablation Study
4.3.1
Component Analysis
Table 2: Performance analysis with different component combinations
We evaluate framework through ablation studies on key components. Table 2 shows moving fusion causes 73.80% Recall @ 20 decrease, while CL and alignment provide 10.45% and 3.49% NDCG@20 improve- ments respectively. Recommendation loss is critical, with its removal causing 69.94% NDCG@20
Variant
Components
Pertormance
Fusion
CL
Align
Rec
Recall
NDCG
DualFuse
0.3282
0.3693
w/o Fusion
/
0.0860
0.0857
wfo CL
0.2983
0.3307
w/o Align
x
0.3231
0.3564
w/o Rec
x
0.2436
0.2586
degradation and scattered embedding distributions (Details see Appendix DD3 and Figure
4.3.2 Impact of Sampling Rate
Table Analysis of sampling rate optimization
Our sampling rate analysis em- pirically validates the theoretical bounds established in Theorem 3
P
Performance
Efficiency
Recall
NDCG
Time/epoch
Conv.
Total
1.0
0.3226
0.3567
75.30s
23
28.9m
through three critical regimes of operation. The full-graph train-
0.1
0.3268
0.3604
68.03s
21
23.8m
0.01
0.3282
0.3693
59.57s
21
20.8m
ing setting (p = 1.0) corre-
0.001
0.2025
0,2050
50.01s
>50
>41.7m
sponds to the Ci/t-dominater warm-up phase in our theoretical framework. While this configuration achieves reasonable performance (0.3226 Recall @ 20), it requires 28.9 minutes total training time,
demonstrating the computational cost of unoptimized stability bounds. This setting serves as our baseline for comparison with sampling-opiimized approaches.
The optimal sampling configuration (p 0.01) represents a critical balance point between the competing terms in our theoretical model. This rate effectively balances the C2()// term (where k/p² is bounded at 10a x k) and the < (p)/ V term from Theorem The experimental results confirm that this configuration delivers the peak performance (0.3282 Recall @ (20) while requiring only 20.8 minutes of training, a 28,0 3800% efficiency gain compared to full-graph training. This empirical finding aligns with the (1/t)) decay advantage predicted by our theoretical analysis.
4.4 Empirical Analysis
Robustness Analysis To evaluate the model robustness, conduct experiments by randomly coruptingg 0%-20% user-item interactions. SDCGCL-Dualusse demonstrates the superior stability, maintaining 81.3% of its performance under 20% noise MovieLens and 76.7% Amazon. As shown in Figure 5 all SDCGCL, variants exhibit improved robustness compared to their base dual-channel architecture and cross-channel calibration.
counterparts, attributed
Parameter Sensitivity Our framework involves five key hyperparameters that control different aspects of model behavior. Through extensive analysis, we observe that moderate values consistently yield optimal performance: channel balancing parameter a (0.1-0.4), contrastive learning parameter 33 (0.3-0.7), distribution alignment parameter 7 1-0.5),,, and negative feedback weight & (0.1-).3). The auxiliary loss parameter À shows stability in the range of 0.1-0.2. Figure 6 visualizes these effects across datasets. These findings confirm our theoretical analysis in Section 2.1.4 particularly regarding the necessity of distribution alignment.

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(c) 10100m
MI.-IM Complete
(b) Fusion
(d) w/o Align
(e) Rec
(i) Yelp Complete
w/o Fusion
(h) w/o CL
( w/o Align
i w/o Rec
(k) Amazon Complete
w/o Fusion
w/o CL
(n) Align
(o) w/o Rec
(p) ML.IOM Complete
(q) w/o Fusion
(r) w/o CL.
(8) w/o Align
(t) w/o Rec
Figure 4: L-SNE visualization of learned embeddings four datasets across different ablation settings. Red/or- ange: positive users/items; blue/green: negative users/items.
ons Recaller
MavieLens NDC
0.39
ESCCO Suathen
EUOSE
BICEE WPIDO.
6.9
0.20
6:3
6.3
0.4
132%
150%
BROUE
6.8
6.0
Bakio
(a) Recall 20 under Different Noise Ratios
(b) NDCG@20 under Different Noise Ratios
Figure 5: Model robustness evaluation on two datasets showing superior performance stability of SDCGCL variants under increasing noise ratios.

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Impact of (b) Impact of
(c) Impact of (d) Imppct of
(e) Impact of
Figure 6: Parameter sensitivity analysis showing impact on Recall @ 20 and NDCG@20 across four datasets.
4.5
More Detailed Experiments
We provide additional experimental results in the appendix, including efficiency analysis (Ap- pendix D.I. performance improvement analysis (Appendix (.2), and extended ablation studies (Appendix D.3) to supplement the main findings.
Related Work
5.1
GNNs for Recommendation
Deep learning 59
especially GNN, has revolutionized recommender systems through their capacity to model complex user-item relationships. Starting with foundational works [33] 35].
the field evolved through message passing innovations like GC-MC (36] and NGCF 40), reaching
sigmificant milestone with LightGCN [16] and GCCF [ Subsequent developments enhanced
theoretical foundations through pre-training [4], filtering mechanisms [58], and multi-view learning
[6]] while temporal modeling advanced through architectures like SRGNN [48, GCE-GNN [44].
and TGSREC [10]. Dynamic patterns were captured by DGCF (4T) TG-MC [ DGSR 60 and
SURGE 3. while contrastive learning emerged as promising direction (2 27 55
53 65| [4]]
further enhanced by transformer integrations heterogeneous feedback types.
42 51 50
However, these methods struggle with
5.2 Sign-aware Recommendation
Sign-aware recommendation systems evolved from foundational explicit feedback methods like user-based CF [63]. PMF [30], and SVD++ [23]. Built upon theoretical foundations in spectral analysis [8] matrix decomposition [19], and balance theory [17 7. contemporary research has
deepened understanding of negative feedback 22 [2] leading to innovations in graph-based systems
29 (4, sampling strategies [8 [9] interactive platforms (62] and sequential models [1
32
While
various approaches have interpreted different user behaviors as negative signals [39
12 45
62
many methods still struggle with effective integration, either excluding negative instances
[54 56
or oversimplifying interactions. Our work addresses these limitations through model-agnostic framework that seamlessly integrates with existing methods while avoiding traditional balance theory
constraints 34
6 Conclusion
In this paper, we propose SDCGCL, novel model-agnostic framework for effectively leveraging negative feedback in recommender systems, along with DualFuse, a specially designed model that maximizes the framework's 's capabilities. Through theoretical analysis and extensive experiments, we demonstrate how our dual-channel architecture, cross-channel distribution calibration mechanism, and adaptive prediction strategy successfully address the fundamental challenges of incorporating neg- ative feedback while maintaining computational efficiency. The framework's 's model -ageos-a nature enables seamless integration with existing graph contrastive learning methods, consistently yielding substantial performance improvements across multiple datasets and baseline models. Our compre- hensive empirical results validate that negative feedback indeed plays a crucial role in enhancing recommendation performance when properly utilized through our proposed framework.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (No. 62372264 and No. 92467203) and Sina Weibo Corp. Chaokun Wang is the corresponding author.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly state the contributions of proposing SDCGCL framework, addressing model compatibility, information exchange, and computa- tional efficiency challenges. Key claims align with theoretical analysis and experimental results in Sections 2
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is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
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Answer: [Yes]
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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
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Justification: The work does not involve high-risk models or sensitive data. SDCGCL recommendation framework evaluated on public rating datasets.
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The answer NA means that the paper poses such risks.
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for Justification: No human subject research or crowdsourcing is involved. Experiments use pre-collected public interaction data.
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A Detailed Proofs
A.1 Complete Proof of Theorem 2.1: Distribution Instability
Proof. We begin by establishing the distributional properties of embeddings derived from positive and negative feedback channels. Given the assumptions stated in the theorem, proceed follows:
21

---

Let us denote x E o E and X = E, o E as the interaction scores in positive and negative channels, respectively. By assumption, these random variables follow distributions with
B[x 14;
Var x o²
(15)
B| μ - 01;
Var[X 820²
where 61 represents the mean shift and 82 2 captures the increased variance in negative feedback distributions.
For the predicted preference score Du.s
(1 + X us k we derive its expectation:
El
= [E[(1 + x - k
(1 + E X XX
=(1 k) k(u+ 01)
(16)
= (1 k)μ - ky Kō1
r kō1
Assuming independence between channels. we derive the variance:
Var[ūu.>] Var[(1 X xX
(1 k)) + 1²2 Var [xl
(1 + 12820²
(17)
[1[1+k]]] (1
= 2h +
= =² + 2k + 1/ 82)
This demonstrates that without distribution alignment, the prediction has a systematic bias of -k0
and inflated variance scaled by 82 2 1.
When applying our cross-channel distribution calibration mechanism (Equation 7 we enforce 51 + 0 and 82 - 1. Consequently:
E x
Var [ar(ū
0² 2k: + + 1)] =1 (1+ 24 + 2
(18)
Thus, the alignment mechanism eliminates the bias term -ōū from the prediction expectation and normalizes the variance to more stable form that depends solely on k rather than the instability parameter 82, completing the proof.
A.2 Complete Proof of Theorem 2.2: Cross-Channel Information Preservation
Proof. We analyze the gradient flow through the cross-channel fusion mechanism to demonstrate that it maintains balanced information flow between positive and negative channels.
Gradient Analysis for Positive Channel:
For the positive channel fusion operation, the gradient with respect to the embedding ct can be expressed as:
Oc
det
(19)
0L OZ.
Oz, Oz,
oz det
des
The first term corresponds to the direct gradient flow within the positive channel, while the second term captures the cross-channel influence through the fusion mechanism.
22

---

From Equation 13 in the main paper, we have:
Z
0,1-
+
(20)
0,1-
+
L
|
1=0
Therefore:
oz,
1
de
L+1
(21)
oz,
1
0
e
det
I
1
dop
||et
The derivative of the normalized vector can be expanded as:
e
e
det
||et
dek
V
c
e
(22)
1
e
I
||e
||
|
where I is the identity matrix.
For simplicity and to understand the upper bound, we can establish:
e
1
(23)
Jet
jett
|et
||
2. Gradient Norm Bounds:
Using the above results, we can now bound the gradient norm:
OC OZ.
OL OZ,
IV C1
OZ do
OZ, det
2L OZ
OL oZ.
+
OZ des
Oz de: UL
(24)
ØL
1
OZ
L 1
Oz,
L +
|
1
(1) oz.
0L
0L
1
L+1
az
|et
Similarly, for the negative channel:
0C
0C
IV
+
(25)
L+1
0Z,
azt
|
3. Establishing Lower Bounds:
We can also establish lower bounds:
23

---

02
0c
V All 2 +1
L
O0.
oz.
|
(26)
1
0c
dc
1
IV C
L
+
O2,
O00
Convergence Analysis:
At convergence, several key conditions are satisfied:
1) The loss gradients with respect contrastive embeddings from both channels become approxi- mately equal:
BC
BC
OZ.
2) The embedding norms from both channels converge to similar magnitudes:
||e||
||e,
||-
Substituting these conditions into our bounds:
ac
0L
V
1 1+
|ef
o2.
L + 1
| ||
Ozo
(27)
1
0C
)
OC
S
2 S
1
82
++1
ee
Oz
Since
0C 82
0c
and
||et
||
R
||e,| convergence, conclude that:
82.
V C CIl
(28)
This demonstrates that our cross-channel fusion mechanism maintains balanced gradient flow between positive and negative channels, ensuring that both channels contribute roughly equally to the learning process despite their potentially different initial characteristics.
A.3 Complete Proof of Theorem 3.1: Two-Stage Stability Bound
Proof. We'll analyze each stage of our optimization process separately.
1. Warmup Stage Analysis (Full-Graph Learning):
During the warmup stage (t S )warm), we train on the complete graph without sampling. Under standard assumptions for gradient- based optimization, the loss function L has L-Lipschitz gradients gradiientzgrraieettss in dense interaction regions where l|e, c:ll2 2 8, the maximum distance between any node embeddings is bounded by D and the learning rate is initialized as and potentially follows schedule.
For gradient descent with these conditions, we have:
(1-1) -tt-- Vo,
(29)
|e! (t (t-1) vi...lVo. ((- (()
By the Lipschitz gradient assumption in dense regions:
I o. (( 11 < L2D
(30)
With learning rate schedule 7/- 7// V +I we get:
Elle(") ( 11 1/ L'D
170
L2D
(31)
t
C₁
24

---

where C1 = D2 is our warmup stage constant.
2. Sampling Stage Analysis (Subgraph Learning):
After the warmup stage (t > Twarm), we transition to training on sampled subgraphs. This introduces two additional sources of variance: Sampling Variance: Due to sampling subset of the graph with rate p and Distribution Divergence: Information loss from potentially missing important negative feedback.
Let's denote the true gradient VL and the sampled gradient as V.. The variance of the sampled gradient can be decomposed as:
Var (L) = E V2 +
E
E
0
K
(P))
(32)
information loss
base variance
sparsity penalty
where o is the base variance from full- full-graph training is a constant that scales the variance inflation from rare negative feedback and v(p) measures the information loss due to potential systematic bias in sampled graphs.
The embedding difference satisfies:
Blle(") e(! 112 = B|| 7/- V Ve, (()
e((-1))1
(33)
V ((0
The expected squared norm of the sampled gradient can decomposed as:
||| c, (()((-)))) Var(V.. ((O(--1))
(34)
V Ve. -30)) - + Var( (()(t-1))
where ((p) represents the bias introduced by sampling, which has magnitude proportional to /v(p). Combining these results:
o
E |e((
c!--1) 1/ < 1
p²)
+ 1/-
vv())
P
no
(L²
2
Ulu
v v(()
(35)
p
p
vt
C2(p)
+ c(p)
Vt
where C2(p) (1, S.tKie) and <(p) h(p))
P
This demonstrates that the embedding differences decay O(1/t) during the warmup phase and as O(1/t) + O(I/VF) during the sampling phase. The additional ((1/Vł) term reflects the trade-off' between sampling efficiency (parameter p) and approximation quality (parameters k and v).
B Detailed Multi-Objective Loss Integration
This section provides comprehensive details on our training approach that integrates recommendation supervision, contrastive learning signals, and distribution alignment to effectively capture both types of feedback patterns.
25

---

Algorithm 2: SDCGCL Optimization Algorithm
Input :Positive graph G*, negative graph 9-, total epochs warm-up epochs Twarm, hyperparametens a, 8, X, 7/s Tjr, learning rate
Output: Trained model parameters è
Initialize model parameters θ randomly:
for = to do
ift < Tuarm then
9, 9
/* Full negative graph for warm-up
else
G. PGRWSamplingG p)
/x Ahm
x/
end
for each mini-hatch
/* Dual-channel embedding generation
Ex Z <- GraphEncoder((S". BB);
ET Z GraphEncodeer( B):
Multi-objective loss computation
Liee (te + aaFrcc
L + (1 3) Z 0 ,27):
Caret <- JS(E* )
/* Jemsen-Shannon divergence */
1+ Total loss with regularization
*/
<- Cree + >(Cai + 7Lain)) + m 10
** Parameter update
θ + Adam(9, VoL,
end
end
return
B.1 Recommendation Loss
We adopt Bayesian Personalized Ranking (BPR) loss across both channels. For positive channel:
C Σ Ino - Da.j)
(36)
(u.t,))E0+
where + contains positive triplets (u, i,5) with user observed item i, and unobserved item j. Similarly, for the negative channel, we can obtain Leeer the overall recommendation loss combines both channels:
Cree = (1 - crect + aLFee
(37)
B.2 Contrastive Loss
Following Equations and we apply InfoNCE loss within each channel. For positive channel:
exp(sim E 1/T) CJ - In where sim((--) is dot product similarity and - controls distribution sharpness. Similarly, for the negative channel, we can obtain 2 the combined loss is:
Σ In exp(sim E Z //T/
(38)
wEll Eveu exmmsim E. ZT)/r)
EJEz exp(sim 2/)/T)
TE
Lat
B) BLG
(39)
B.3 Distribution Alignment Loss
For Inter- Cher-Channel Distribution Alignment (Equations
we use Jensen Shannon divergence:
Laist ESS(
JS(
E
E
E,
E
JS{
E
E TT
Σ
E,
E. I ||
|
|
DEll
SENT
IEN
SET
WEN+
NENT
(40)
26

---

where normalized interaction scores correspond to neighborhood aggregation terms and JS divergence serves g" (.)-).
B.4
Joint Training Objective
The final training objective combines all three components with balanced weighting parameters:
L Free + X(Ca + daist) + 1
(41)
where controls the overall contribution of the auxiliary objectives, weights the distribution alignment constraint, and 17 the L2 regularization coefficient applied to model parameters O.
During training, we implement a two-stage optimization strategy where the full negative graph G used for the initial T Tarrm epochs (warm-up stage), followed by efficient Popularity-Guide Random Walk Sampling approach for subsequent epochs. This warm-up period establishes stable initial representations while the sampling stage significantly reduces computational costs without sacrificing model performance.
Empirically, found that setting T Twarm provides sufficient initialization while minimizing overhead, enabling our model to achieve superior results with reduced training time. The sampling rate P controls the subgraph size and directly affects the efficiency-qualee trade-off as demonstrated in Section
4.3
The complete optimization process is formally presented in Algorithm
C
Detailed Experimental Setup
C.1
Datasets
We evaluate our method on four publicly available recommendation datasets: Yelp (business reviews), ens Amazon (book reviews), and Moviel ens (movie ratings) with varying scales, as detailed Table I Following established conventions 42
56 43 we binarize all numerical ratings by considering
scores 2 4 as positive feedback and scores < 4 as negative feedback. Each dataset is rigorously partitioned into training, validation, and test sets using a 7:1:2 ratio prevent data leakage and ensure reproducible evaluation.
Table 4: Statistics of datasets. #Pos/#Neg refers to the percentage of positive and negative samples.
Dataset
#Users
#ltems
#Interaction
#Pos/#Neg
Yelp"
29,601
24,734
2,074,594
66.3%//3.779 7
Amazon2
35,736
38,121
1,960,674
80.6%
ML-IM
6,040
3,706
1,000,209
57.5%
ML-10M²
69,878
10,677
10,000,054
58.9%
C.2 Metrics
For performance evaluation, we adopt two standard ranking metrics: Recall@K, which measures the ratio of correctly recommended items over all ground truth items, and NDCG@K @.K K. which considers both the hit ratio and position of correctly recommended items. Following previous studies on we set K = 20 in our experiments.
graph-based recommendation
16 57
C.3 Baselines
In our experimental evaluation, we benchmark our SDCGCL framework against a diverse set of 22 contemporary recommendation methods. These approaches can be divided into two main categories.
Unsigned RS: Traditional methods like MF (33 and NCF [1] focus basic collaborative filtering. Graph-based approaches including NGCF 40), LightGCN [16], and DGCF
27

---

41
leverage various graph neural architectures. Advanced frameworks such as HyRec
38
GFormer (24). and SelfGNN [8 explore specialized structures. Recent contrastive
learning methods (NCL 27 SGL (46.. LightGCI, 22, XSimGCI. [57]. IGCL [3]
enhance representation learning through different augmentation strategies.
Sign-aware RS:
Early approaches (SiReN (34, SiGRec (21], DFGNN 49 focus on
separate processing of positive and negative feedback. transformer-baseee methods including
SignGT [4] SGFormer [47]. and SIGformer [6] leverage attention mechanisms. Other
specialized architectures like SBGNN (20), SLGNN [25], and NFARec
43 explore unique
graph structures and operators for signed feedback.
C.4 Experiment Setting
For our SDCGCL, we adopt the Adam optimizer and employ grid search for hyperparameter op- timization. Specifically, we set the hidden embedding dimension d to 64. The learning rate is set to 10 with a batch size of 2048. The hyperparameter search ranges are E [0. & E [0, 1]. y [0. 1]. k E [0, 1] and [0.05, 0.35]. For computational efficiency, T Twarm uniformly set 1. For other baseline models, we strictly follow their officially released code to ensure fair comparison. All experiments in this paper are conducted on 8 RTX3090 GPUs.
D Additional Experiments
D.1
Computational Efficiency Analysis
Our SDCGCL framework introduces minimal computational overhead (3-8s per epoch) while signif- icantly improving convergence speed across different base models. SDCGCL-DualFuse achieves the best efficiency with the fastest convergence (21 epochs) and lowest total training time (20.8 minutes) on ML-1M dataset, benefiting from both the enhanced learning signals of negative feedback and efficient architecture design. Compared to the base models (SGL, LightGCL, XSimGCL), our framework consistently reduces the overall training time by improving convergence despite the slight increase in per-epoch processing time.
We provide a detailed analysis of the time complexity for our framework and its comparison with other methods.
Table Runtime comparison across methods ML-IM dataset.
Method
Time/epoch
Epochs
Total time
SGL
81.09s
32
43.25m
SDCGCL-SGL
89.57s
27
40.3
LightGCL
66.09s
23
25.3m
SDCGGL-LightGCL
69.57s
24
27.83m
XSimGGL
56.09s
25
23.3m
SDCGCL-XSImGCL
59.34s
23
22.74m
SDCGCL-DualFur
59.57s
21
20.8m
Table 6: Detailed time complexity comparison across methods. q: preserved features in LightGCI SVD;
augmentation ratio in SGL; Po: the two-stage optimization weighting coefficient; M I + [T]: total nodes.
Method
Augmicattion
Convolution
BPR
Contrast
Align
Time/Epoch
SGL.
O Fid 4nj8 (Ta)
O12Ma
O(Md)
81.09
SDCGCL-SGL
0 2000/E
O + 40)((8" I po18 (ad)
0(2M
O(Md) O(Md)
89.57s
LightGCL
O,2.E" [Ld 2qMLa)
0(2Ma)
O(M4)
LightLL
0(2[( pa 1 [ 4gMLd)
((2Me)
O(Md)
O(Md)
69.57s
XSInGCL
0 La)
0(2Ma)
O[Md)
56.0%
SDCGCL-XSimGCLL
0 20gle 2pole-(La) Laf)
0(2Md)
O(Md)
O(Md)
SDCGGL--
0 [d 2polE La)
((2M))
O(Md)
O(Md)
59.57s
As a preprocessing step, SDCGCL employs negative graph sampling strategy with complexity O(kpM), where k denotes the number of sampled neighbors and p represents the sampling ratio. We define Po as the two-stage optimization weighting coefficient, whose value is (epoch T Tuarm))
T Tuarm- Since T Tworm = 1 < epoch, therefore po R p.
28

---

The additional computational overhead introduced by SDCGCL varies across different base models, with complexity O(2polE Ld) for XSimGCL, O(2qMLd) for LightGCL, and ((2apolE Ld) for SGL.. DualFuse, specifically designed for efficient implementation of the SDCGCL framework, maintains the same simplified convolution complexity 0 [ + 2pole - as SDCGCL. XSimGCL while avoiding additional operations like feature decomposition or augmentation required by other variants.
The theoretical analysis of time complexity can only reveal the complexity of each epoch, while the actual running time is also affected by factors such as convergence speed. Our empirical results in Table | demonstrate that SDCGCL framework consistently improves training efficiency across different base models by introducing minimal computational overhead while significantly accelerating convergence in most cases.
D.2 Performance Gains Analysis
Table 7 presents comprchensive analysis of performance enhancements achieved by the SDCGCL framework when integrated with existing recommendation approaches. The empirical evidence demonstrates consistent and substantial improvements across multiple datasets and baseline architec- tures.
Table 7: Performance enhancement analysis of the SDCGCL framework relative to baseline models. Results are reported as Recall @ 20/NDCG@ 20 pairs, with improvement percentages indicating relative performance gains across metrics.
Dataset
Base Model
Original
SDCGCL-Enhance
Improvement (%)
SGL
0.2798/0.3037
0.2879/0.3300
-2.89%//48.6655
ML-IM
LighiGCL
0.2730/0.3035
0.2945/0.3423.
+7.88%//12.7888
XSimGCL
0.2729/0.3087
0.3050/0.3401
+11.76%
SGL
0.0746/0.0729
0.1136/0.0907
+52.28%%/424.4256
Yelp
LightGCL.
0.0697/0.0675
1.166/000826
+53.37%
XSimGCI.
0.0867/0.0758
0.1112/0.088
+28.269%/416.2399
SGL
0.0958/0.0694
0.1108/0. 1001
+15.60%
Amazon
LightGCL
0.0967/0.072
0.1057/0. 08090
+9.31%
XSimGCI.
0.0963/0.0707
0. 1.11// 1014
118.59%/44434455
SGL
0.3056/0.3295
0.3768/0.3716
+23.30% 12.664
ML-10M
LighTGCL
0.3098/0.3231
0.3810/0.3766
+22.98%%/+16.379
XSimGCI.
0.3109/0.3371
0.3791/0.3726
+21.949%/410.558
D.3 Extended Ablation Analysis
We evaluate the contribution of each key component by conducting ablation studies on: (1) cross- channel fusion ("w/o Fusion") from the DualFuse base model, and three components from SDCGCL framework: (2) contrastive learning ("w/o CL"), (3) distribution alignment ("w/o Align"), and (4) recommendation loss ("w/o Rec"). Figure and Table g present the visualization and quantitative results across all datasets. The experimental results reveal that cross-channel fusion
Table Ablation study on different components of SDCGCL. The base model the DualFuse, results are highlighted bold.
ML-IM
Yelp
Amazon
ML-I0M
Variant
Recall
NDCG
Recall
NDCG
Recall
NDCG
Recall
NDCG
DualFuse
0,3282
0,3693
0.1243
0.0959
0.1342
0.1113
0.3900
0,3860
w/o Fusion
0.0860
0.0857
0.1046
0.0894
0.1274
0.1063
0.1624
0.1494
wfo CL
0.2983
0.3307
0.0969
0.0835
0.1161
0.0950
0.3780
0.3699
w/o Align
0.3231
0.3564
0.1032
0.0896
0.1297
0.1086
0.3847
0.3751
Rec 0.2436
0.2586
0.0793
0.0667
0.0680
0.0530
0.1535
0.1389
exhibits dataset-dependent impact. Its removal causes severe performance degradation on MovieLens (73.80% decrease in Recall @ on ML-1M, 58.36% decrease on ML-10M) but moderate impact on Yelp (15.85% decrease) and minimal impact Amazon (5.07% decrease). This suggests that cross-channel fusion particularly critical for datasets with dense user-item interaction patterns like MovieLens.
29

---

The contrastive learning component consistently contributes to model performance across all datasets, with its removal causing 10.45% decrease in NDCG@20 on ML-IM, 12.93% on Yelp, 14.65% on Amazon, and 4.17% on ML-10M. Similarly, distribution alignment shows moderate but consistent impact across datasets, with performance drops of 3.49% on ML.1M,, 6.57% on Yelp. 2.43% on Amazon, and 2.82% on ML-10M when removed.
Most importantl, the recommendation loss proves essential for effective training across all datasets, as its removal leads to randomly scattered embedding distributions (Figure A[e) and the most substantial performance drops: 69. 69.94% decrease NDCG@20 on ML-IM, 30.45% on Yelp, 52.38% on Amazon, and 64.02% on ML-10M. This demonstrates the fundamental role of supervised signals in learning discriminative representations for different types of feedback, regardless of dataset characteristics.
E Limitations and Future Work
While our proposed SDCGCL framework demonstrates significant improvements across multiple benchmarks, there are several avenues for future exploration. The current implementation has been validated on public benchmark datascts, but deployment in surge-scale industrial recommender systems might introduce additional complexities that deserve further investigation. Additionally, the framework could be extended to capture dynamic evolution of user feedback patterns over time, which might further enhance recommendation performance in dynamic environments. Future work could also explore the integration of explicit explanation mechanisms to improve user experience and trust, well adaptation strategies for extreme cold-start scenarios where both positive and negative feedback signals are initially limited.
Broader Impacts
Our work focuses on enhancing both the performance and efficiency of recommender systems through effective utilization of negative feedback, thereby benefiting the overall development of recommendation technologies. The proposed framework improves user experience across various digital platforms including e-commerce, social media, and content streaming services. We do not foresee any negative impacts resulting from our work.
30


Reviews:
[{"review_id": "3RXFzA2CZc", "reviewer": "Reviewer_w8U5", "summary": "This 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", "questions": "See weakness.", "limitations": "See weakness.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: \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", "was_revised": false, "final_justification": "", "rebuttal": "**Reviewer Comment:** \n\n> 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**Response:** \n\nThank you for this important question. While our experiments use explicit rating datasets as examples, **our SDCGCL framework is fully applicable to implicit feedback systems.** For instance, **datasets like KuaiRec [1] and KuaiRand [2] demonstrate how implicit behavioral data can be converted to binary positive/negative feedback**: KuaiRec classifies feedback based on viewing duration ratios (ratios ≥4 as positive, <0.1 as negative), while KuaiRand uses click behaviors (\"is_click\" attribute for positive/negative classification). Our dual-channel architecture only requires binary interaction classifications, making it directly compatible with modern recommendation platforms that primarily collect implicit signals like YouTube, TikTok, and Spotify. The core technical components, dual-channel graph embedding and cross-channel distribution calibration, operate independently of whether the binary labels originate from explicit ratings or converted implicit behaviors.\n\nTo demonstrate this, we evaluated our method on **KuaiRec** [1] and **KuaiRand** [2] datasets, which contain real user interactions from a video platform with implicit feedback:\n\n| Method          | KuaiRec [1] |         | KuaiRand [2] |         |\n| --------------- | ----------- | ------- | ------------ | ------- |\n| | Recall@20   | NDCG@20 | Recall@20    | NDCG@20 |\n| SGL | 0.0854      | 0.0503  | 0.1292       | 0.0644  |\n| SGL+SDCGCL | 0.0901      | 0.054   | 0.1495       | 0.0722  |\n| improv. | 5.50%       | 7.36%   | 15.71%       | 12.11%  |\n| LightGCL | 0.0833      | 0.0508  | 0.1276       | 0.0613  |\n| LightGCL+SDCGCL | 0.0899      | 0.0541  | 0.1453       | 0.0689  |\n| improv. | 7.92%       | 6.50%   | 13.87%       | 12.40%  |\n| XSimGCL | 0.0871      | 0.0528  | 0.1296       | 0.0652  |\n| XSimGCL+SDCGCL  | 0.0927      | 0.0552  | 0.1509       | 0.0748  |\n| improv. | 6.43%       | 4.55%   | 16.44%       | 14.72%  |\n\n[1] Gao C, Li S, Lei W, et al. KuaiRec: A fully-observed dataset and insights for evaluating recommender systems[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 540-550.\n\n[2] Gao C, Li S, Zhang Y, et al. Kuairand: An unbiased sequential recommendation dataset with randomly exposed videos[C]//Proceedings of the 31st ACM international conference on information & knowledge management. 2022: 3953-3957.\n\n**Reviewer Comment:** \n\n> 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? \n\n**Response:** \n\nThank you for this important concern. In practice, the δ₂ ≥ 1 assumption is well-founded: negative feedback naturally exhibits higher variance due to user behavioral heterogeneity,and some users readily express negative opinions while others completely avoid giving negative ratings [1], creating inherent distribution instability. This contrasts with positive feedback, where users more consistently engage with content they enjoy.\n\nTo further validate this assumption, **we empirically computed the variance ratios of user-item interaction scores (E⁺ᵤ ○ E⁺ᵢᵀ and E⁻ᵤ ○ E⁻ᵢᵀ) across our experimental datasets after model training**. The results confirm our theoretical foundation:\n\n| Dataset | δ₂ (Var[E⁻ᵤ ○ E⁻ᵢᵀ]/Var[E⁺ᵤ ○ E⁺ᵢᵀ]) |\n| ------- | ------------------------------------ |\n| ML-1M   | 1.74 |\n| Yelp    | 3.42 |\n| Amazon  | 2.89|\n| ML-10M  | 1.61|\n\nAll ratios exceed 1.0, with review-based platforms (Yelp, Amazon) showing particularly high variance ratios due to more polarized user rating behaviors. Our distribution alignment mechanism (Equation 6) automatically calibrates for varying δ₂ values, ensuring robustness across different variance characteristics.\n\n[1] Wu, Yiqing, et al. \"DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback.\" *Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining*. 2024.\n\n**Reviewer Comment:** \n\n> 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**Response:** \n\nThank you for this important question about our sampling strategy. We provide intuitive reasoning and empirical validation for why popularity-guided random walk sampling is effective in recommendation scenarios.\n\nOur sampling strategy is specifically designed for negative feedback graphs where information concentrates on high-degree nodes (popular items with mixed reviews) while maintaining structural coherence through random walks. Pure random sampling fails to capture the heavy-tailed importance distribution, while pure random walks may get trapped in low-importance regions. Our exponential weighting with temperature parameter τ and guided walk strategy balances information prioritization with sample diversity.\n\nWe conducted comprehensive comparisons across all datasets:\n\n| Sampling Method              | ML-1M      |            | Yelp       |            | Amazon     |            | ML-10M     |            |\n| - | - | - | - | -| - | -| ---------- | ---------- |\n|  | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    |\n| Uniform sampling (ρ=0.01)    | 0.3249     | 0.3621     | 0.1192     | 0.0921     | 0.1313     | 0.1098     | 0.3854     | 0.3802     |\n| Pure random walk (ρ=0.01)    | 0.3256     | 0.3679     | 0.1211     | 0.0926     | 0.1328     | 0.1101     | 0.3878     | 0.3833     |\n| Importance sampling (ρ=0.01) | 0.3251     | 0.3658     | 0.1235     | 0.0932     | 0.1325     | 0.1102     | 0.3852     | 0.3805     |\n| **Our method (ρ=0.01)**      | **0.3282** | **0.3693** | **0.1243** | **0.0959** | **0.1342** | **0.1113** | **0.3900** | **0.3860** |\n\n\n\n**Reviewer Comment:** \n\n> Most recent state-of-the-art methods in REC graph learning and contrastive learning approaches are missing, potentially overstating relative performance.\n\n> [1] Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective NeruIPS 2023 Zhang et al.\n\n> [2] How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective WSDM 2025 Lin et al.\n\n**Response:** \n\nThank you for highlighting these recent state-of-the-art methods. We have implemented and evaluated both suggested approaches. Following their original implementations, we integrated them with the **XSimGCL backbone** for comprehensive comparison:\n\n| Dataset | ML-1M |  | Yelp| | Amazon|| ML-10M||\n| -------------------------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n| Method | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    |\n| XSimGCL| 0.2729     | 0.3087     | 0.0867     | 0.0758     | 0.0963     | 0.0707     | 0.3109     | 0.3371     |\n| GCFlogdet-XSimGCL [1] | 0.2688     | 0.3104     | 0.0788     | 0.0699     | 0.0971     | 0.0715     | 0.3122     | 0.3383     |\n| ReSN-XSimGCL [2]  | 0.2765     | 0.3166     | 0.0882     | 0.0737     | 0.1014     | 0.0772     | 0.3201     | 0.3438     |\n| SDCGCL-XSimGCL | 0.3050     | 0.3401     | 0.1112     | 0.0881     | 0.1142     | 0.1014     | 0.3791     | 0.3726     |\n| SDCGCL-DualFuse  | **0.3282** | **0.3693** | **0.1243** | **0.0959** | **0.1342** | **0.1113** | **0.3900** | **0.3860** |\n\n**Reviewer Comment:** \n\n> 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\n**Response:** \n\nThank you for this important concern. **The binary threshold (≥4 positive, <4 negative) follows well-established conventions in signed recommendation literature.** This classification scheme has been extensively validated and adopted in prior work, including SiGRec [1], Pone-GNN [2], SiReN [3], and SigFormer [4], demonstrating its acceptance as a standard practice for converting explicit ratings to binary feedback signals.\n\nMoreover, our framework's core contribution lies in the dual-channel architecture and cross-channel distribution calibration, which effectively leverages any reasonable binary classification of user preferences. **The method's effectiveness stems from properly handling the distributional differences between positive and negative signals, rather than relying on a specific threshold choice.** Our approach addresses the fundamental challenge of integrating dual-polarity feedback regardless of the specific labeling scheme used.\n\n[1] Huang J, Xie R, Cao Q, et al. Negative can be positive: Signed graph neural networks for recommendation[J]. Information Processing & Management, 2023, 60(4): 103403.\n\n[2] Liu Z, Wang C, Zheng S, et al. Pone-GNN: integrating positive and negative feedback in graph neural networks for recommender systems[J]. ACM Transactions on Recommender Systems, 2025, 3(2): 1-23.\n\n[3] Seo C, Jeong K J, Lim S, et al. SiReN: Sign-aware recommendation using graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(4): 4729-4743.\n\n[4] Chen S, Chen J, Zhou S, et al. SIGformer: Sign-aware graph transformer for recommendation[C]//Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval. 2024: 1274-1284."}, {"review_id": "A4fSS36CBO", "reviewer": "Reviewer_3Jaw", "summary": "This 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.", "questions": "Please refer to the concerns raised in the Weaknesses section.", "limitations": "Please see the Weaknesses section", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \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.", "was_revised": true, "final_justification": "After reading the other reviewers' comments and the rebuttal from the authors, I have no further questions. I think the positive score has reflected the contributions of this paper, thus I will keep the score.", "rebuttal": "**Reviewer Comment:** \n\n> W1: The model introduces several important hyper-parameters. Although the paper includes a sensitivity analysis, the number and significance of these parameters may increase the tuning burden in real-world deployments. \n\n**Response:** \n\nThank you for raising this practical concern. Our extensive sensitivity analysis demonstrates that the introduced hyperparameters exhibit **stable behavior across diverse datasets and do not create significant tuning burden**. Performance remains robust within broad parameter ranges, with degradation typically under 10% even at range boundaries. Most importantly, using uniform default values across all datasets achieves consistently strong performance without dataset-specific tuning, making deployment straightforward and practical.\n\nSpecifically, our experiments (Section 4.5, Figures 7&10) show that optimal parameter ranges remain remarkably consistent across four datasets with vastly different characteristics: α∈[0.1,0.4], β∈[0.3,0.7], γ∈[0.1,0.5], k∈[0.1,0.3], λ∈[0.1,0.2]. These datasets vary significantly in size (3.7K to 38K items), sparsity, and negative feedback ratios (19.4% to 42.5%), yet the parameter stability persists. Using default values (α=0.2, β=0.5, γ=0.3, k=0.2, λ=0.1) yields competitive results across all datasets without any tuning. This inherent robustness ensures that practitioners can deploy our framework efficiently with minimal parameter adjustment.\n\n\n\n**Reviewer Comment:** \n\n> W2: 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. \n\n**Response:** \n\nThank you for this insightful observation. The crescent-shaped hollow region is **not an intentional design** but rather an emergent property arising from the **sparsity of negative feedback signals**. Since negative feedback is inherently sparser than positive feedback (as shown in Table 4, negative feedback ratios range from 19.4% to 42.5%), users who predominantly provide negative ratings naturally cluster in specific regions of the embedding space, forming the observed crescent pattern around a hollow center.\n\n**This phenomenon actually validates our model's effectiveness**. The clear spatial separation between user groups demonstrates that our dual-channel framework successfully captures and distinguishes different user behavioral patterns—those who actively express dislikes cluster differently from those who primarily provide positive feedback. This distinct embedding structure directly contributes to our superior recommendation performance by enabling more accurate identification of user preference types. The consistency of this pattern across all datasets and ablation settings further confirms that our model reliably learns meaningful representations that reflect the fundamental differences in how users express preferences, particularly regarding negative feedback.\n\n\n\n**Reviewer Comment:** \n\n> W3: The size of font in Figure 5 is too small, which affects the readability.\n\n> Paper Formatting Concerns: The width of the tables in the main text is not consistent. In some cases, tables are displayed alongside the main text, which does not align with the typical formatting style of academic conference papers.\n\n**Response:** \n\nThank you for pointing out these formatting issues. We acknowledge that Figure 5's font size is currently too small and that table widths are inconsistent due to space constraints in the 9-page submission format.\n\nThe NeurIPS camera-ready version allows 10 pages compared to the current 9-page limit. In the final version, we will utilize this additional space to: (1) increase the font size in Figure 5 and all other figures to ensure proper readability, and (2) standardize all table widths to span the full column width following proper academic formatting conventions. These adjustments will significantly improve the visual presentation and readability of our paper."}, {"review_id": "84IzTW8bq1", "reviewer": "Reviewer_VDQo", "summary": "This 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.", "questions": "In 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?", "limitations": "Yes, 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "The response have addressed my concerns and improved my understanding of the paper, including Eq.6, Eq.7,  and the choice of the sampling rate ρ.", "rebuttal": "**Reviewer Comment:** \n\n> Regarding the choice of the sampling rate ρ... it does not detail whether the adaptability of this parameter differs across various datasets.\n\n**Response:** \n\nThank you for highlighting this important point about sampling rate adaptability. **We have conducted comprehensive sampling rate experiments on the ML-1M dataset.** In **Section 4.3 (Table 3)**, we presented the main results showing that ρ=0.01 achieves the best performance-efficiency trade-off, reducing training time by 20.9% (from 28.9m to 20.8m) while achieving the highest Recall@20 (0.3282) and NDCG@20 (0.3693). In **Appendix E.3.3**, we provided detailed theoretical validation, demonstrating how different ρ values empirically confirm our Theorem 3: ρ=1.0 corresponds to the warm-up phase, ρ=0.01 balances the C₂(ρ)/t and ε(ρ)/√t terms optimally, while ρ=0.001 violates stability conditions leading to 61.8% performance degradation.\n\nTo further validate the generalizability of ρ selection across diverse dataset characteristics, we conducted experiments with different sampling rates on all datasets. The results confirm that ρ=0.01 consistently achieves optimal or near-optimal performance:\n\n| ρ     | Yelp       |            | Amazon     |            | ML-10M     |            |\n| ----- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |\n|       | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    | Recall@20  | NDCG@20    |\n| 1.0   | 0.1198     | 0.0921     | 0.1285     | 0.1067     | 0.3842     | 0.3798     |\n| 0.1   | 0.1225     | 0.0944     | 0.1318     | 0.1092     | 0.3876     | 0.3834     |\n| 0.01  | **0.1243** | **0.0959** | **0.1342** | **0.1113** | **0.3900** | **0.3860** |\n| 0.001 | 0.0892     | 0.0685     | 0.0976     | 0.0812     | 0.2847     | 0.2789     |\n\n\n\n**Reviewer Comment:** \n\n> The DualFuse Model is missing an overall flowchart.\n\n**Response:** \n\nThank you for this valuable suggestion. **The core mechanism of DualFuse is illustrated in Figure 3**, which shows the graph fusion process where embeddings from positive and negative channels are normalized and fused to generate contrastive views. The complete DualFuse pipeline involves an additional dual-channel graph encoder step (as described in Section 2.2.1) followed by this fusion mechanism.\n\nWe acknowledge that including a comprehensive flowchart showing the end-to-end process from input graphs through dual-channel encoding to the final fusion would provide readers with a clearer understanding of the complete model architecture. However, according to NeurIPS rebuttal guidelines, we cannot provide additional figures in this response. We will incorporate such a flowchart in the revised version to better illustrate how the dual-channel graph encoder feeds into the cross-channel fusion mechanism, making the overall DualFuse workflow more accessible to readers.\n\n\n\n**Reviewer Comment:** \n\n> In Figure 2, the \"Negative Graph Sampling Pool\" section lacks a corresponding explanation.\n\n**Response:** \n\nThank you for highlighting this clarification need. The \"Negative Graph Sampling Pool\" is detailed in **Algorithm 1** (page 6), which generates strategically sampled subgraphs from the negative graph G^- during preprocessing using popularity-guided random walks. This pool stores multiple pre-computed negative subgraphs that preserve the most informative feedback patterns while significantly reducing computational cost, achieving 20.9% faster training with comparable performance as shown in Table 3. Rather than sampling from the full negative graph at each training iteration, our approach pre-constructs a diverse collection of representative subgraphs that capture essential negative feedback signals. In the revised version, **we will add a brief explanation below Figure 2**: *\"The Negative Graph Sampling Pool contains pre-sampled subgraphs of G^- generated by Algorithm 1, enabling efficient training by avoiding redundant sampling during each epoch.\"*\n\n\n\n**Reviewer Comment:** \n\n> Why does Equation 6 ensure that $\\delta_1 \\to 0$ and $\\delta_2 \\to 1$， A proof is missing.\n\n**Response:**\n\nThank you for this important theoretical question. Let me provide a rigorous proof for why Equation 6 ensures $\\delta_1 \\to 0$ and $\\delta_2 \\to 1$.\n\nEquation 6 minimizes the alignment objective $\\mathcal{L}_ {\\text{align}} = \\sum_{u \\in U} \\text{JS}\\left(\\bar{S}^+_u, \\bar{S}^-_u\\right)$ where $\\bar{S}^+ _u = \\frac{1}{|N^+_u|}\\sum _{i \\in N^+_u} E^+_u \\circ E^{+\\top}_i$ and $\\bar{S}^-_u = \\frac{1}{|N^-_u|}\\sum _{j \\in N^-_u} E^-_u \\circ E^{-\\top}_j$ represent the neighborhood-aggregated similarities for positive and negative channels respectively.\n\nThe Jensen-Shannon divergence $\\text{JS}(P,Q) = \\frac{1}{2}D_{KL}(P||M) + \\frac{1}{2}D_{KL}(Q||M)$ where $M = \\frac{P+Q}{2}$ reaches its global minimum of zero if and only if $P = Q$ almost surely. Therefore, at convergence, our optimization enforces $\\bar{S}^+_u = \\bar{S}^-_u$ almost surely for all users $u$.\n\nFor the mean alignment, this convergence condition implies $\\mathbb{E}[\\bar{S}^+_u] = \\mathbb{E}[\\bar{S}^-_u]$. By linearity of expectation, we have $\\mathbb{E}\\left[\\frac{1}{|N^+_u|}\\sum _{i \\in N^+_u} E^+_u \\circ E^{+\\top}_i\\right] = \\mathbb{E}\\left[\\frac{1}{|N^-_u|}\\sum _{j \\in N^-_u} E^-_u \\circ E^{-\\top}_j\\right]$. Under our sampling strategy that ensures balanced neighborhood construction, the neighborhoods $N^+_u$ and $N^-_u$ constitute representative samples of the user's interaction patterns. By the law of large numbers, as neighborhood sizes become sufficiently large, the sample averages converge to population expectations: $\\lim _{|N^+_u| \\to \\infty} \\frac{1}{|N^+_u|}\\sum _{i \\in N^+_u} E^+_u \\circ E^{+\\top}_i = \\mathbb{E}[E^+_u \\circ E^{+\\top}_i]$ and similarly for the negative channel. Therefore, we obtain $\\mathbb{E}[E^+_u \\circ E^{+\\top}_i] = \\mathbb{E}[E^-_u \\circ E^{-\\top}_j]$. Substituting the definitions from Theorem 1 yields $\\mu = \\delta_1 + \\mu$, which directly implies $\\delta_1 = 0$.\n\nFor the variance alignment, the convergence condition similarly enforces $\\text{Var}[\\bar{S}^+_u] = \\text{Var}[\\bar{S}^-_u]$. Using the variance formula for sample means, this becomes $\\frac{\\text{Var}[E^+_u \\circ E^{+\\top}_i]}{|N^+_u|} = \\frac{\\text{Var}[E^-_u \\circ E^{-\\top}_j]}{|N^-_u|}$. Our dual-channel sampling strategy is specifically designed to maintain comparable neighborhood sizes, ensuring $\\mathbb{E}[|N^+_u|/|N^-_u|] \\to 1$ as the sampling process balances both channels. Under this balanced sampling regime, we obtain $\\text{Var}[E^+_u \\circ E^{+\\top}_i] = \\text{Var}[E^-_u \\circ E^{-\\top}_j]$. Substituting from Theorem 1 gives us $\\sigma^2 = \\delta_2\\sigma^2$, which directly implies $\\delta_2 = 1$.\n\n**Reviewer Comment:** \n\n> Equation 7 seems problematic... only use portion of negative feedback (k can be 0)... does not address the second challenge...\n\n**Response:**\n\nThank you for raising these two important points. Let me address each concern separately.\n\n**Regarding the subtraction in Equation 7**: Let me clarify why the subtraction is mathematically correct and why the suggested addition would lead to incorrect predictions. Our training objectives optimize similarity scores through contrastive learning (Equations 4 and 5), which results in the following learned representations at convergence:\n\n$\\mathbb{E}[E^+_u \\circ E^+_i | (u,i) \\in G^+] \\gg \\mathbb{E}[E^+_u \\circ E^+_i | (u,i) \\notin G^+]$\n\n$\\mathbb{E}[E^-_u \\circ E^-_i | (u,i) \\in G^-] \\gg \\mathbb{E}[E^-_u \\circ E^-_i | (u,i) \\notin G^-]$\n\nThis means both channels learn to produce high similarity scores for their respective interaction types. To clarify the fundamental issue with addition: if we used $\\hat{y}_{u,i} = (1-k)E^+_u \\circ E^+_i + kE^-_u \\circ E^-_i$, consider a disliked item where $(u,i) \\in G^-$. We would have:\n\n- $E^+_u \\circ E^+_i$: small value (since $(u,i) \\notin G^+$)\n- $E^-_u \\circ E^-_i$: **large value** (since $(u,i) \\in G^-$)\n- Result: $(1-k) \\cdot \\text{small} + k \\cdot \\text{large} = \\text{moderate to high score}$\n\nThis would assign **high scores to items users explicitly dislike**, which is fundamentally incorrect for recommendations. With our subtraction formulation $\\hat{y}_{u,i} = (1+k)E^+_u \\circ E^+_i - kE^-_u \\circ E^-_i$:\n\n- For liked items: $(1+k) \\cdot \\text{large} - k \\cdot \\text{small} = \\text{high score}$ \n- For disliked items: $(1+k) \\cdot \\text{small} - k \\cdot \\text{large} = \\text{low score}$ \n\nThis correctly captures user preferences, where negative feedback **reduces** the prediction score. Our Theorem 1 provides theoretical support by proving that negative feedback naturally introduces a negative bias term $-k\\delta_1$ in the prediction expectation, mathematically validating the subtraction design.\n\n**Regarding full utilization of negative feedback**: Our framework comprehensively leverages negative feedback through multiple integrated mechanisms beyond just the prediction equation. The dual-channel architecture ensures negative feedback is fully encoded throughout the model. Even when $k=0$, negative feedback still influences the model through cross-channel fusion (Equation 10) where $Z^+_u$ incorporates information from $e^-_u$, and distribution alignment (Equation 6) which shapes the entire representation space. **Most importantly, we empirically prove k≠0 is optimal.** Figure 7(d) and Section 4.5 show that k=0 causes up to 5% performance degradation, with optimal k∈[0.1,0.3] across all datasets. This demonstrates that our framework not only **can** but **must** utilize negative feedback for optimal performance, fully addressing the \"partial utilization\" challenge."}, {"review_id": "ZiHaS8YrXk", "reviewer": "Reviewer_tuXv", "summary": "The 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.", "questions": "My 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", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "After the rebuttal phase, after reading the other reviews, and authors' responses to those, I decide to confirm my initial score I gave to the paper.\n\nSpecifically, all my concerns have been discussed and addressed by the authors.\n\nWhatever the final outcome for this paper, I think it will benefit quite a lot from the fruitful discussion we had along with the authors, the reviewers, and the Area Chair. I believe the paper will improve its quality a lot in any case.", "rebuttal": "**Reviewer Comment:** \n\n> Q1&W2: 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**Response:** \n\nThank you for this excellent clarification question. Our φ(·) function in Equation (3) serves as a **general interface** that captures augmented embeddings from different base models, regardless of how the underlying augmentation is performed.\n\nThe key design principle is model compatibility. When our framework integrates with SGL, the base model continues to perform its edge dropout on adjacency matrices during message passing, and φ(·) captures the resulting embeddings computed from the modified graph structure. Similarly, when integrated with LightGCL, φ(·) represents embeddings derived from SVD-reconstructed graphs, while with XSimGCL, it captures embeddings after embedding-level perturbations.\n\nThis abstraction enables our model-agnostic framework to work seamlessly with different augmentation strategies without forcing base models to change their preferred approaches. Each method maintains its original augmentation mechanism (structural or embedding-based), while φ(·) provides a unified interface for our dual-channel processing and cross-channel fusion.\n\nWe acknowledge that our current mathematical notation using φ(·) may lead to some ambiguity in interpretation. In the revised version, we will adopt a clearer formulation such as: \n\n$z^+_ {u,l^* } = φ(\\hat{A}^+, e^+_ {u,l }, X, θ), \\quad z^-_ {u,l^*} = φ(\\hat{A}^-, e^-_ {u,l }, X, θ)$\n\n$Z^+_ u = \\text{AGG}^* \\left(\\{z^+_ {u,l^* }\\}\\right), \\quad Z^-_ u = \\text{AGG}^* \\left(\\{z^-_ {u,l^*}\\}\\right)$\n\nwhere φ(·) represents the base model's specific augmentation mechanism with parameters: $\\hat{A}$ is the adjacency matrix, $e_{u,l}$ is the embedding at layer $l$, $X$ is the optional node feature matrix, and $θ$ contains augmentation-specific parameters (e.g., dropout rates, sampling ratios). Note that $l$ is not necessarily equal to $l^*$, as they may represent different layers in the model architecture.\n\n\n\n**Reviewer Comment:** \n\n> Q2&W3: 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**Response:** \n\nThank you for this insightful question. We conducted the requested experiments evaluating APLT@20 and EFD@20 metrics.\n\nOur popularity-guided sampling operates on the **negative graph**, which fundamentally differs from traditional popularity-based approaches. Since our prediction uses $\\hat{y} = (1+k)E^+ - kE^-$, **items with high popularity in the negative graph are actually penalized.** Random sampling fails to capture this crucial negative signal distribution, leading to suboptimal diversity. Specifically, for an item $i$ with negative feedback from popular users, random sampling has probability $\\rho$ of including these signals, while our method uses a **sampling pool** with weights $P(i) \\propto \\exp(d_i/\\tau)$ where $d_i$ is the degree. The sampling pool ensures diverse representation by allowing temperature-controlled exploration beyond the most popular items. This mechanism properly captures items frequently appearing in negative interactions (often mainstream items disliked by diverse user groups) and **subsequently penalizes** them in the final prediction, creating space for long-tail items.\n\nWe compared three strategies on MovieLens-1M:\n\n| Method                   | APLT@20   | EFD@20    |\n| ------------------------ | --------- | --------- |\n| Full graph (ρ=1.0)       | 0.242     | 0.159     |\n| Random sampling (ρ=0.01) | 0.241     | 0.163     |\n| Our method (ρ=0.01)      | **0.249** | **0.174** |\n\n\n\n**Reviewer Comment:** \n\n> W1: 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> W4: 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\n**Response:** \n\nThank you for these constructive suggestions regarding paper organization and readability. We appreciate your consideration of the page limitations while identifying areas for improvement. We will add a dedicated background/preliminaries section to introduce graph notations and embedding fundamentals, making the paper more accessible to readers from diverse backgrounds. Additionally, we will reposition Table 1 to align better with its corresponding text section for improved readability. The camera-ready version allows for an additional page (10  pages in total), which will provide sufficient space to implement these enhancements without compromising the technical content. These revisions will strengthen the paper's clarity and accessibility while maintaining its technical rigor.\n\n\n\n**Reviewer Comment:** \n\n> Suggestion: I would further suggest the authors take a look at this quite novel recommendation dataset [1] with incorporates actual negative feedback from users. I think it could be very useful to further analyze the goodness of their methodology.\n\n**Response:** \n\nThank you for suggesting the KuaiRec dataset. We have conducted experiments on both **KuaiRec** [1] and **KuaiRand** [2] datasets, which contain actual negative feedback from users. The results further validate the effectiveness of our SDCGCL framework:\n\n| Method          | KuaiRec [1] |         | KuaiRand [2] |         |\n| --------------- | ----------- | ------- | ------------ | ------- |\n|                 | Recall@20   | NDCG@20 | Recall@20    | NDCG@20 |\n| SGL             | 0.0854      | 0.0503  | 0.1292       | 0.0644  |\n| SGL+SDCGCL      | 0.0901      | 0.054   | 0.1495       | 0.0722  |\n| improv.         | 5.50%       | 7.36%   | 15.71%       | 12.11%  |\n| LightGCL        | 0.0833      | 0.0508  | 0.1276       | 0.0613  |\n| LightGCL+SDCGCL | 0.0899      | 0.0541  | 0.1453       | 0.0689  |\n| improv.         | 7.92%       | 6.50%   | 13.87%       | 12.40%  |\n| XSimGCL         | 0.0871      | 0.0528  | 0.1296       | 0.0652  |\n| XSimGCL+SDCGCL  | 0.0927      | 0.0552  | 0.1509       | 0.0748  |\n| improv.         | 6.43%       | 4.55%   | 16.44%       | 14.72%  |\n\n[1] Gao C, Li S, Lei W, et al. KuaiRec: A fully-observed dataset and insights for evaluating recommender systems[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 540-550.\n\n[2] Gao C, Li S, Zhang Y, et al. Kuairand: An unbiased sequential recommendation dataset with randomly exposed videos[C]//Proceedings of the 31st ACM international conference on information & knowledge management. 2022: 3953-3957."}]

Author rebuttal:

Meta-review: This paper proposes SDCGCL (Signed Dual-Channel Graph Contrastive Learning), a framework for leveraging both positive and negative feedback in recommender systems through dual-channel graph embedding and cross-channel distribution calibration. The work received scores of 3, 4, 4, and 5, but the review process was significantly compromised by one reviewer's complete non-engagement. Key assessment: While the paper demonstrates strong technical merit and comprehensive experimental validation, the review process integrity issues prevent a clear acceptance decision. (1) Technical contributions and experimental rigor: The paper addresses an important and understudied problem in recommendation systems—effectively utilizing negative feedback alongside positive signals. The dual-channel architecture with cross-channel distribution calibration is technically sound and theoretically grounded. The experimental evaluation is comprehensive, spanning multiple datasets, architectures, and including thorough ablation studies. The model-agnostic design enabling integration with existing graph contrastive learning methods (SGL, LightGCL, XSimGCL) demonstrates practical value. (2) Strong majority reviewer support: Three reviewers (tuXv, VDQo, 3Jaw) provided positive assessments after thorough evaluation. Reviewer tuXv (score 5) praised the framework as "technically solid" with "high impact," confirming their "initial positive judgment." Reviewer VDQo (score 4) stated they were "very satisfied with clarifications" and explicitly raised their evaluation score. Reviewer 3Jaw (score 4) confirmed "no further questions" and maintained their positive score. (3) Exceptional author responsiveness: The authors provided comprehensive rebuttals addressing all raised concerns with substantial additional work: implementing suggested baselines (GCFlogdet, ReSN), conducting experiments on implicit feedback datasets (KuaiRec/KuaiRand) showing 5.50%-16.44% improvements, providing mathematical proofs for theoretical assumptions (δ₂≥1 validated across all datasets), and demonstrating diversity metrics (APLT@20: 0.249, EFD@20: 0.174). (4) Critical review process failure: Reviewer w8U5 (score 3) raised legitimate concerns about implicit feedback compatibility, theoretical assumptions, missing baselines, and binary threshold justification. However, despite the authors' comprehensive responses addressing each concern with new experiments and theoretical validation, this reviewer completely disengaged from the review process, violating NeurIPS mandatory reviewer obligations and ignoring multiple follow-ups and Area Chair reminders. This prevents proper assessment of whether the substantial author responses resolved the technical concerns. (5) Resolution assessment: Based on the quality and comprehensiveness of the author responses, the technical concerns appear to have been thoroughly addressed. The additional experiments on implicit feedback datasets directly validate practical applicability, the mathematical proofs support theoretical foundations, and the implemented baselines confirm competitive performance. The framework demonstrates both theoretical rigor and practical effectiveness across diverse evaluation scenarios.
Decision: Accept (poster)