pith. sign in

arxiv: 2405.16772 · v2 · submitted 2024-05-27 · 💻 cs.SI · cs.LG

Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

Xin He , Wenqi Fan , Ruobing Wang , Yili Wang , Ying Wang , Shirui Pan , Xin Wang This is my paper

Pith reviewed 2026-05-24 01:37 UTC · model grok-4.3

classification 💻 cs.SI cs.LG
keywords social recommendationpopularity biassocial network denoisingcondition-guided diffusiondebiased recommendationsuser social preferencesrecommendation model
0
0 comments X

The pith

CGSoRec mitigates popularity bias in social recommendations by denoising networks and using adjusted preferences as conditions in a diffusion model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Social recommendation models incorporate user social ties to personalize suggestions, yet these ties tend to amplify bias toward already popular items while embedding much redundant information. The paper develops CGSoRec to counter this by first applying a Condition-Guided Social Denoising Model that removes meaningless social relations, then reweighting the resulting social preferences so they actively offset the bias present in the base recommender. These reweighted preferences are supplied as control conditions to a Condition-Guided Diffusion Recommendation Model that steers output generation toward less biased results. A reader would care because the method keeps social signals useful for personalization while reducing the tendency to over-recommend hot items at the expense of long-tail ones, and the authors report gains on three real-world datasets.

Core claim

CGSoRec mitigates the model's popularity bias by denoising the social network and adjusting the weights of user's social preferences, with the adjusted preferences introduced as conditions to control the recommendation results for a debiased direction.

What carries the argument

Condition-Guided Social Recommendation Model (CGSoRec) that combines a Condition-Guided Social Denoising Model (CSD) to filter redundant relations with a Condition-Guided Diffusion Recommendation Model (CGD) that treats reweighted social preferences as control conditions.

If this is right

  • Denoising removes redundant social relations so that users' social preferences with items can be captured more precisely.
  • Reweighting social preferences produces signals that can counteract the popularity bias already present in the recommendation model.
  • Treating the adjusted preferences as conditions in the diffusion model steers generation toward a debiased recommendation direction.
  • The overall pipeline yields measurable gains on three real-world datasets compared with prior social recommendation approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same denoising-plus-conditioning pattern could be tested on non-social recommenders to see whether it corrects other forms of exposure bias.
  • If the reweighting step proves stable, the method offers a route to turn social graphs from bias amplifiers into explicit bias correctors without discarding the graph entirely.
  • Dynamic social networks might require periodic re-denoising; experiments could check whether the adjustment remains effective when ties evolve over time.

Load-bearing premise

That removing redundant social relations and reweighting social preferences will reliably counteract popularity bias without discarding useful preference signal or introducing new distortions.

What would settle it

An experiment on the three datasets in which the model shows no increase in long-tail item exposure or no reduction in popular-item dominance relative to baselines that use the full unadjusted social network.

Figures

Figures reproduced from arXiv: 2405.16772 by Ruobing Wang, Shirui Pan, Wenqi Fan, Xin He, Xin Wang, Yili Wang, Ying Wang.

Figure 1
Figure 1. Figure 1: Popularity bias comparison between introduced and removed Social Network [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Average frequency of items rec￾ommended based on a user’s social prefer￾ences. Recently, with the development of diffusion models in the field of recommendation systems, some stud￾ies [19, 20, 21, 22] have shown that diffusion recommendation models can model the complex interactions be￾tween users and items more accurately compared to traditional matrix factor￾ization recommendation models and graph neural… view at source ↗
Figure 3
Figure 3. Figure 3: The overall framework of the proposed method. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The Joint Inferencing process of the proposed method. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Frequency of different item groups recommended by DiffNet, LightGCN-S, [PITH_FULL_IMAGE:figures/full_fig_p028_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Effect of µ and λ on CGSoRec w.r.t Recall@10. of CGSoRec. Smaller values of µ and λ often lead to better model perfor￾mance. Overall, CGSoRec exhibits robustness to these two hyper-parameters on the LastFM dataset. Although the optimal choice of hyper-parameters varies across different datasets, larger values of µ and λ generally lead to a sharp decline in performance. This phenomenon occurs across all thr… view at source ↗
Figure 7
Figure 7. Figure 7: We can see that, in lastFM and Ciao dataset, as the value of [PITH_FULL_IMAGE:figures/full_fig_p032_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Effect of ωs on CGSoRec w.r.t NDCG@10. 5.4.4. Effect of hyper-parameter ωs (RQ4) The hyper-parameter ωs as formulated in Eq. 20 controls the degree to which the condition-guided recommendation result is involved in the final 32 [PITH_FULL_IMAGE:figures/full_fig_p032_8.png] view at source ↗
read the original abstract

Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant information that is essentially meaningless for the model's performance. Existing social recommendation models often integrate the entire social network directly, with little effort to filter or adjust social information to mitigate popularity bias introduced by the social network. In this paper, we propose a Condition-Guided Social Recommendation Model (named CGSoRec) to mitigate the model's popularity bias by denoising the social network and adjusting the weights of user's social preferences. More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely. Then, CGSoRec calculates users' social preferences based on denoised social network and adjusts the weights in users' social preferences to make them can counteract the popularity bias present in the recommendation model. At last, CGSoRec includes a Condition-Guided Diffusion Recommendation Model (CGD) to introduce the adjusted social preferences as conditions to control the recommendation results for a debiased direction. Comprehensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes CGSoRec, a social recommendation model that first applies a Condition-Guided Social Denoising Model (CSD) to remove redundant relations from the social network, then computes and reweights users' social preferences to counteract popularity bias, and finally feeds the adjusted preferences as conditions into a Condition-Guided Diffusion Recommendation Model (CGD) to steer generation toward debiased outputs. Experiments on three real-world datasets are reported to demonstrate effectiveness over existing social recommenders.

Significance. If the central mechanism is shown to work without discarding useful preference signal, the approach would address a recognized limitation of social recommenders (amplification of popularity bias via unfiltered social edges) by combining denoising with explicit conditioning; this could be of interest to the social-recommendation and fairness communities provided the adjustment rule is made explicit and reproducible.

major comments (3)
  1. [Abstract / §3] Abstract and §3 (method overview): the claim that the reweighted social preferences 'can counteract the popularity bias present in the recommendation model' is stated without a derivation showing how the weight adjustment is computed from item popularity statistics or why it is guaranteed not to over-correct long-tail items; this step is load-bearing for the debiasing guarantee.
  2. [§4] §4 (CGD component): the diffusion process is conditioned on the adjusted social preferences, yet no analysis is supplied demonstrating that the conditioning term selectively reduces popular-item influence rather than simply shifting the overall distribution; without this, the causal link from CSD+weight adjustment to lower popularity bias remains unverified.
  3. [Experiments] Experiments section: while three datasets are mentioned, the abstract and method description supply neither the exact popularity-bias metric (e.g., ARP, Gini, or long-tail coverage), nor ablation results isolating the contribution of the weight-adjustment step versus the denoising step alone; this prevents verification that the claimed mitigation follows from the proposed operations rather than from other modeling choices.
minor comments (2)
  1. [§3] Notation for the social-preference weight vector and the conditioning variable in CGD should be introduced once and used consistently; current description mixes 'adjusted social preferences' and 'conditions' without an explicit mapping equation.
  2. [§2] The paper should include a short related-work paragraph contrasting CSD with prior social-denoising techniques (e.g., graph denoising or attention-based filtering) to clarify the novelty of the condition-guided aspect.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and insightful comments on our manuscript. We address each of the major comments point by point below, indicating where revisions will be made to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract / §3] Abstract and §3 (method overview): the claim that the reweighted social preferences 'can counteract the popularity bias present in the recommendation model' is stated without a derivation showing how the weight adjustment is computed from item popularity statistics or why it is guaranteed not to over-correct long-tail items; this step is load-bearing for the debiasing guarantee.

    Authors: We acknowledge the need for a more explicit derivation of the weight adjustment mechanism. In the revised manuscript, we will expand §3 to include a detailed derivation of how the weights are adjusted based on item popularity statistics. We will also provide reasoning or bounds demonstrating that the adjustment does not over-correct long-tail items, thereby supporting the debiasing claim. revision: yes

  2. Referee: [§4] §4 (CGD component): the diffusion process is conditioned on the adjusted social preferences, yet no analysis is supplied demonstrating that the conditioning term selectively reduces popular-item influence rather than simply shifting the overall distribution; without this, the causal link from CSD+weight adjustment to lower popularity bias remains unverified.

    Authors: We agree that additional analysis is required to verify the selective effect of the conditioning. In the revision, we will augment §4 with an analysis of the conditioning term, including perhaps a theoretical examination or empirical study (e.g., through attention maps or distribution shifts) to show its selective impact on reducing popular-item influence. revision: yes

  3. Referee: [Experiments] Experiments section: while three datasets are mentioned, the abstract and method description supply neither the exact popularity-bias metric (e.g., ARP, Gini, or long-tail coverage), nor ablation results isolating the contribution of the weight-adjustment step versus the denoising step alone; this prevents verification that the claimed mitigation follows from the proposed operations rather than from other modeling choices.

    Authors: We appreciate this feedback on the experimental presentation. The revised version will explicitly define the popularity-bias metrics used in the experiments (including ARP, Gini coefficient, and long-tail coverage where applicable). Additionally, we will include new ablation studies that separately evaluate the contributions of the weight-adjustment step and the denoising step (CSD) to isolate their individual impacts on bias mitigation. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation chain is self-contained with independent components

full rationale

The paper introduces CSD for social denoising and CGD for condition-guided diffusion, with an explicit weight adjustment step on social preferences. No equations or self-citations are quoted that reduce the bias-mitigation claim to a fitted parameter renamed as prediction, a self-defined quantity, or a load-bearing self-citation chain. The central premise (denoising plus reweighting steers recommendations) is presented as a modeling choice whose validity is tested experimentally rather than derived by construction from the inputs. This is the normal case of an independent methodological proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, mathematical axioms, or postulated entities; the model components are named at the level of high-level design choices only.

pith-pipeline@v0.9.0 · 5794 in / 1106 out tokens · 33510 ms · 2026-05-24T01:37:08.022773+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

49 extracted references · 49 canonical work pages · 2 internal anchors

  1. [1]

    J. Yu, H. Yin, J. Li, Q. Wang, N. Q. V. Hung, X. Zhang, Self-supervised multi-channel hypergraph convolutional network for social recommenda- tion, in: Proceedings of the web conference 2021, 2021, pp. 413–424

    work page 2021

  2. [2]

    Y. Quan, J. Ding, C. Gao, L. Yi, D. Jin, Y. Li, Robust preference-guided denoising for graph based social recommendation, in: Proceedings of the ACM Web Conference 2023, 2023, pp. 1097–1108

    work page 2023

  3. [3]

    Lacker, S

    B. Lacker, S. F. Way, Socially-motivated music recommendation, in: Proceedings of the International AAAI Conference on Web and Social Media, Vol. 18, 2024, pp. 879–890

    work page 2024

  4. [4]

    W. Ma, Y. Wang, Y. Zhu, Z. Wang, M. Jing, X. Zhao, J. Yu, F. Tang, Madm: A model-agnostic denoising module for graph-based social rec- ommendation, in: Proceedings of the 17th ACM International Confer- ence on Web Search and Data Mining, 2024, pp. 501–509

    work page 2024

  5. [5]

    J. Wu, W. Fan, J. Chen, S. Liu, Q. Li, K. Tang, Disentangled con- trastive learning for social recommendation, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Manage- ment, 2022, pp. 4570–4574. 34

    work page 2022

  6. [6]

    McPherson, L

    M. McPherson, L. Smith-Lovin, J. M. Cook, Birds of a feather: Ho- mophily in social networks, Annual review of sociology 27 (1) (2001) 415–444

    work page 2001

  7. [7]

    Zheng, Z

    P. Zheng, Z. Huang, Y. Dou, Y. Yan, Rumor detection on social media through mining the social circles with high homogeneity, Information Sciences 642 (2023) 119083

    work page 2023

  8. [8]

    X. Sha, Z. Sun, J. Zhang, Y.-S. Ong, Who wants to shop with you: Joint product–participant recommendation for group-buying service, IEEE Transactions on Neural Networks and Learning Systems (2022)

    work page 2022

  9. [9]

    H. Dong, W. Wang, K. Huang, F. Coenen, Automated social text anno- tation with joint multilabel attention networks, IEEE Transactions on Neural Networks and Learning Systems 32 (5) (2020) 2224–2238

    work page 2020

  10. [10]

    W.-D. Xi, L. Huang, C.-D. Wang, Y.-Y. Zheng, J.-H. Lai, Deep rating and review neural network for item recommendation, IEEE Transactions on Neural Networks and Learning Systems 33 (11) (2021) 6726–6736

    work page 2021

  11. [11]

    T. Wei, F. Feng, J. Chen, Z. Wu, J. Yi, X. He, Model-agnostic counter- factualreasoningforeliminatingpopularitybiasinrecommendersystem, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 1791–1800

    work page 2021

  12. [12]

    L. Wu, P. Sun, Y. Fu, R. Hong, X. Wang, M. Wang, A neural influence diffusion model for social recommendation, in: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, 2019. 35

    work page 2019

  13. [13]

    J. Tang, H. Gao, H. Liu, mtrust: Discerning multi-faceted trust in a con- nected world, in: Proceedings of the fifth ACM international conference on Web search and data mining, 2012, pp. 93–102

    work page 2012

  14. [14]

    Zheng, C

    Y. Zheng, C. Gao, X. Li, X. He, Y. Li, D. Jin, Disentangling user in- terest and conformity for recommendation with causal embedding, in: Proceedings of the Web Conference 2021, 2021, pp. 2980–2991

    work page 2021

  15. [15]

    Y. Li, J. Liu, J. Ren, Social recommendation model based on user in- teraction in complex social networks, PloS one 14 (7) (2019) e0218957

    work page 2019

  16. [16]

    Orgad, B

    H. Orgad, B. Kawar, Y. Belinkov, Editing implicit assumptions in text- to-image diffusion models, in: Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, 2023, pp. 7053–7061

    work page 2023

  17. [17]

    K. Meng, D. Bau, A. Andonian, Y. Belinkov, Locating and editing fac- tual associations in gpt, Advances in Neural Information Processing Sys- tems 35 (2022) 17359–17372

    work page 2022

  18. [18]

    Editing factual knowledge in language models

    N. De Cao, W. Aziz, I. Titov, Editing factual knowledge in language models, arXiv preprint arXiv:2104.08164 (2021)

    work page arXiv 2021

  19. [19]

    W. Wang, Y. Xu, F. Feng, X. Lin, X. He, T.-S. Chua, Diffusion recom- mender model, arXiv preprint arXiv:2304.04971 (2023)

    work page arXiv 2023

  20. [20]

    Z. Yang, J. Wu, Z. Wang, X. Wang, Y. Yuan, X. He, Generate what you prefer: Reshaping sequential recommendation via guided diffusion, Advances in Neural Information Processing Systems 36 (2024). 36

    work page 2024

  21. [21]

    Walker, T

    J. Walker, T. Zhong, F. Zhang, Q. Gao, F. Zhou, Recommendation via collaborative diffusion generative model, in: International Conference on Knowledge Science, Engineering and Management, Springer, 2022, pp. 593–605

    work page 2022

  22. [22]

    J. Choi, S. Hong, N. Park, S.-B. Cho, Blurring-sharpening process mod- els for collaborative filtering, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, pp. 1096–1106

    work page 2023

  23. [23]

    Tashiro, J

    Y. Tashiro, J. Song, Y. Song, S. Ermon, Csdi: Conditional score-based diffusion models for probabilistic time series imputation, Advances in Neural Information Processing Systems 34 (2021) 24804–24816

    work page 2021

  24. [24]

    Dhariwal, A

    P. Dhariwal, A. Nichol, Diffusion models beat gans on image synthesis, Advances in neural information processing systems 34 (2021) 8780–8794

    work page 2021

  25. [25]

    Zhang, A

    L. Zhang, A. Rao, M. Agrawala, Adding conditional control to text-to- image diffusion models, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 3836–3847

    work page 2023

  26. [26]

    J. Ho, T. Salimans, Classifier-free diffusion guidance, arXiv preprint arXiv:2207.12598 (2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  27. [27]

    J. Yu, Y. Wang, C. Zhao, B. Ghanem, J. Zhang, Freedom: Training- free energy-guided conditional diffusion model, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 23174–23184. 37

    work page 2023

  28. [28]

    J. Yu, H. Yin, J. Li, M. Gao, Z. Huang, L. Cui, Enhancing social recom- mendation with adversarial graph convolutional networks, IEEE Trans- actions on knowledge and data engineering 34 (8) (2020) 3727–3739

    work page 2020

  29. [29]

    W. Wang, F. Feng, X. He, L. Nie, T.-S. Chua, Denoising implicit feed- back for recommendation, in: Proceedings of the 14th ACM interna- tional conference on web search and data mining, 2021, pp. 373–381

    work page 2021

  30. [30]

    Jamali, M

    M. Jamali, M. Ester, A matrix factorization technique with trust prop- agation for recommendation in social networks, in: Proceedings of the fourth ACM conference on Recommender systems, 2010

    work page 2010

  31. [31]

    W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, D. Yin, Graph neural networks for social recommendation, in: Proc. of WWW, 2019

    work page 2019

  32. [32]

    G. Guo, H. Zhou, B. Chen, Z. Liu, X. Xu, X. Chen, Z. Dong, X. He, Ipgan: Generating informative item pairs by adversarial sampling, IEEE transactions on neural networks and learning systems 33 (2) (2020) 694– 706

    work page 2020

  33. [33]

    W. Fan, T. Derr, Y. Ma, J. Wang, J. Tang, Q. Li, Deep adversarial social recommendation, in: 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, 2019, pp. 1351–1357

    work page 2019

  34. [34]

    X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, M. Wang, Lightgcn: Sim- plifying and powering graph convolution network for recommendation, in: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020, pp. 639–648. 38

    work page 2020

  35. [35]

    L. Wu, H. Lin, C. Tan, Z. Gao, S. Z. Li, Self-supervised learning on graphs: Contrastive, generative, or predictive, IEEE Transactions on Knowledge and Data Engineering (2021)

    work page 2021

  36. [36]

    H.-S. Sheu, Z. Chu, D. Qi, S. Li, Knowledge-guided article embedding refinement for session-based news recommendation, IEEE Transactions on Neural Networks and Learning Systems 33 (12) (2021) 7921–7927

    work page 2021

  37. [37]

    J. Yu, H. Yin, J. Li, M. Gao, Z. Huang, L. Cui, Enhance social recom- mendation with adversarial graph convolutional networks, IEEE Trans- actions on Knowledge and Data Engineering (2020)

    work page 2020

  38. [38]

    D. P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv preprint arXiv:1312.6114 (2013)

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  39. [39]

    N. Li, B. Guo, Y. Liu, Z. Yu, Disentangled-feature and composite-prior vae on social recommendation for new users, Expert Systems with Ap- plications 247 (2024) 123309

    work page 2024

  40. [40]

    L. Li, J. Xiahou, F. Lin, S. Su, Distvae: distributed variational autoen- coder for sequential recommendation, Knowledge-Based Systems 264 (2023) 110313

    work page 2023

  41. [41]

    J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Ad- vances in neural information processing systems 33 (2020) 6840–6851

    work page 2020

  42. [42]

    Xuan, Diffusion cross-domain recommendation, arXiv preprint arXiv:2402.02182 (2024)

    Y. Xuan, Diffusion cross-domain recommendation, arXiv preprint arXiv:2402.02182 (2024). 39

    work page arXiv 2024

  43. [43]

    Y. Wang, Z. Liu, L. Yang, P. S. Yu, Conditional denoising diffusion for sequential recommendation, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2024, pp. 156–169

    work page 2024

  44. [44]

    Liang, R

    D. Liang, R. G. Krishnan, M. D. Hoffman, T. Jebara, Variational au- toencoders for collaborative filtering, in: Proceedings of the 2018 world wide web conference, 2018, pp. 689–698

    work page 2018

  45. [45]

    Z. Zhou, S. Tulsiani, Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 12588–12597

    work page 2023

  46. [46]

    Zhong, D

    Z. Zhong, D. Rempe, D. Xu, Y. Chen, S. Veer, T. Che, B. Ray, M. Pavone, Guided conditional diffusion for controllable traffic simu- lation, in: 2023 IEEE International Conference on Robotics and Au- tomation (ICRA), IEEE, 2023, pp. 3560–3566

    work page 2023

  47. [47]

    arXiv preprint arXiv:2208.11970 , year=

    C. Luo, Understanding diffusion models: A unified perspective, arXiv preprint arXiv:2208.11970 (2022)

    work page arXiv 2022

  48. [48]

    Cantador, A

    I. Cantador, A. Bellogín, D. Vallet, Content-based recommendation in social tagging systems, in: Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 237–240

    work page 2010

  49. [49]

    Y. Lu, Y. Fang, C. Shi, Meta-learning on heterogeneous information networks for cold-start recommendation, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 1563–1573. 40

    work page 2020