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arxiv: 2606.05621 · v1 · pith:DNLPE5RNnew · submitted 2026-06-04 · 💻 cs.IR

ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation

Xiangming Li , Hua Chu , Chengyu Feng , Jianan Li , Yangtao Zhou This is my paper

Pith reviewed 2026-06-27 23:52 UTC · model grok-4.3

classification 💻 cs.IR
keywords recommendation denoisingnoise simulationagent-based frameworksupervised learningimplicit feedbackuser behavior simulationnoise recognitionrecommender systems
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The pith

Recommendation denoising shifts from heuristics to supervised learning by creating labeled noise examples with an agent simulator.

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

The paper proposes a Creation-Recognition paradigm that proactively generates labeled noisy interactions instead of inferring them indirectly. An agent-based framework called ANCHOR simulates user behaviors in a recommender-in-the-loop setup to produce both out-of-preference noise via five extensible mechanisms and boundary-adjacent noise via adversarial refinement. These labeled examples then train a parametric recognizer that combines collaborative signals and semantic representations to identify noise in real data. This reformulation addresses the absence of explicit noise annotations in implicit feedback by turning denoising into a supervised task. The approach aims to avoid the generalization failures and high costs of prior heuristic or side-information methods.

Core claim

By adopting a recommender-in-the-loop agentic architecture to synthesize diverse out-of-preference noise through five extensible simulation mechanisms and informative boundary-adjacent noise through adversarial boundary refinement, the framework generates labeled noisy interactions that train a reusable parametric recognizer integrating collaborative and semantic signals, thereby transforming recommendation denoising from heuristic filtering into supervised learning.

What carries the argument

The Creation-Recognition paradigm, in which an agentic simulator proactively generates labeled noise for training a dedicated recognizer.

If this is right

  • Denoising becomes a supervised learning problem with explicit labels rather than unsupervised inference.
  • The generated labels cover both diverse out-of-preference noise and challenging boundary-adjacent cases.
  • The resulting recognizer can be reused on real interaction data to clean user preference signals.
  • True user preferences are better preserved by reducing misidentification of noise.

Where Pith is reading between the lines

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

  • The same creation step could supply training data for noise detection in other implicit-feedback domains such as search logs or social media.
  • If the recognizer proves reusable across datasets, it could lower dependence on dataset-specific heuristics in production systems.
  • Integrating the agent with more advanced user-behavior models might further close the gap between simulated and real noise distributions.

Load-bearing premise

The noise distributions produced by the agent's simulation mechanisms and boundary refinement are close enough to real-world noisy implicit feedback for the recognizer to generalize.

What would settle it

A benchmark experiment where the trained recognizer is applied to a dataset containing known real noise annotations and fails to outperform strong heuristic baselines on noise identification accuracy or downstream recommendation metrics.

Figures

Figures reproduced from arXiv: 2606.05621 by Chengyu Feng, Hua Chu, Jianan Li, Xiangming Li, Yangtao Zhou.

Figure 1
Figure 1. Figure 1: Comparison between traditional denoising methods [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall framework of the proposed ANCHOR framework. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Impact comparison w.r.t. noise ratio in added inter￾action data. 5.4 Hyperparameter Sensitivity (RQ3) We analyze ANCHOR’s sensitivity to three hyperparameters, as shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hyperparameter Analysis of ANCHOR. demonstrates that explicitly simulating realistic noise provides far superior supervision than heuristic sampling, verifying ANCHOR’s core philosophy. Similarly, the degradation in w/o ABR highlights the importance of boundary-adjacent noise as informative hard negatives for calibrating the decision boundary. Furthermore, w/o Sem shows that semantic cues are essential for… view at source ↗
read the original abstract

Distilling accurate user preferences from noisy implicit feedback remains a fundamental bottleneck in recommendation systems, highlighting the need for recommendation denoising. However, real-world data lack explicit noise annotations, forcing existing methods to rely on unsupervised side information or handcrafted heuristics. These approaches often incur high external costs, generalize poorly, or depend on unreliable priors, causing noise misidentification and corrupting true user preference representations. To address these limitations, we propose a paradigm-level reformulation of recommendation denoising. Instead of indirectly inferring noisy interactions through heuristics, our Creation-Recognition paradigm proactively creates labeled noisy interactions and trains a dedicated recognizer to identify them, transforming denoising from heuristic filtering into supervised learning. Based on this paradigm, we present ANCHOR, an agent-based framework inspired by recent LLM-as-User research. ANCHOR simulates user behaviors to generate realistic noise labels and enables supervised denoising through two stages: noise creation and noise recognition. In the noise creation stage, ANCHOR adopts a recommender-in-the-loop agentic architecture to synthesize both diverse out-of-preference noise and informative boundary-adjacent noise. For out-of-preference noise, it implements five extensible simulation mechanisms to approximate major sources of noisy implicit feedback. For boundary-adjacent noise, an adversarial boundary refinement mechanism generates ambiguous interactions that challenge the recognizer and target the decision boundary. In the noise recognition stage, ANCHOR leverages the generated labels to train a reusable parametric recognizer that integrates collaborative signals and semantic representations to detect noise patterns in real interaction data.

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

2 major / 0 minor

Summary. The paper proposes the Creation-Recognition paradigm for recommendation denoising, arguing that real-world implicit feedback lacks noise labels and forces reliance on heuristics. It introduces ANCHOR, an LLM-as-User agentic framework with a recommender-in-the-loop architecture. In the noise creation stage, five extensible simulation mechanisms generate out-of-preference noise while an adversarial boundary refinement produces boundary-adjacent noise; the resulting labeled synthetic interactions are then used in the noise recognition stage to train a parametric recognizer that combines collaborative signals and semantic representations for application to real data.

Significance. If the synthetic noise distributions prove representative of real-world sources, the shift from heuristic filtering to supervised recognition could reduce dependence on unreliable priors and improve denoising fidelity. The manuscript offers a conceptually coherent framework but contains no experiments, ablation studies, downstream recommendation metrics, or validation of simulation fidelity, so its practical significance remains unassessable from the presented material.

major comments (2)
  1. [Abstract] Abstract and noise creation stage: the central claim that labels produced by the five simulation mechanisms plus adversarial refinement are sufficiently representative for the recognizer to generalize rests on an unverified distributional match; the manuscript provides no experiments, distributional comparisons, or downstream performance results to test this assumption.
  2. [Noise recognition stage] Noise recognition stage: the description of the reusable parametric recognizer that integrates collaborative signals and semantic representations lacks any architectural details, training procedure, or loss formulation, rendering the supervised-learning transformation impossible to evaluate or reproduce.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for empirical validation and technical specificity. We address the major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and noise creation stage: the central claim that labels produced by the five simulation mechanisms plus adversarial refinement are sufficiently representative for the recognizer to generalize rests on an unverified distributional match; the manuscript provides no experiments, distributional comparisons, or downstream performance results to test this assumption.

    Authors: We agree that the representativeness of the synthetic noise labels is a central assumption requiring verification. The submitted manuscript focuses on defining the Creation-Recognition paradigm and the agentic architecture of ANCHOR. In the revision we will add (1) distributional comparisons between the generated noise and real-world implicit feedback patterns (using available proxy metrics such as interaction entropy or category deviation) and (2) downstream recommendation experiments that measure denoising impact on standard ranking metrics. revision: yes

  2. Referee: [Noise recognition stage] Noise recognition stage: the description of the reusable parametric recognizer that integrates collaborative signals and semantic representations lacks any architectural details, training procedure, or loss formulation, rendering the supervised-learning transformation impossible to evaluate or reproduce.

    Authors: We acknowledge that the current description of the noise recognition stage remains at a high level. The revised manuscript will specify the recognizer architecture (e.g., a hybrid model combining graph-based collaborative embeddings with text-derived semantic features), the supervised training procedure on the synthetic labeled set, and the concrete loss function (binary cross-entropy with optional contrastive regularization) used to train the noise detector. revision: yes

Circularity Check

0 steps flagged

No circularity: framework is a novel construction without self-referential derivations or fitted predictions.

full rationale

The paper proposes a new Creation-Recognition paradigm and ANCHOR framework for generating synthetic noise labels via agentic simulation and training a recognizer. No equations, parameters fitted to data subsets, or self-citations are presented as load-bearing for a derivation. The central claim is the construction itself, not a reduction of a result to prior fitted inputs or self-cited uniqueness theorems. This matches the default expectation of a self-contained proposal against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The framework rests on the unverified assumption that LLM-driven user simulation can faithfully reproduce the statistical properties of real noisy implicit feedback; no free parameters, axioms, or invented entities are explicitly quantified in the abstract.

pith-pipeline@v0.9.1-grok · 5808 in / 1102 out tokens · 17273 ms · 2026-06-27T23:52:37.603937+00:00 · methodology

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Reference graph

Works this paper leans on

47 extracted references · 4 canonical work pages · 1 internal anchor

  1. [1]

    Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate. 2023. Out of one, many: Using language models to simulate human samples.Political Analysis31, 3 (2023), 337–351

    2023

  2. [2]

    Georg Buscher, Ludger Van Elst, and Andreas Dengel. 2009. Segment-level display time as implicit feedback: a comparison to eye tracking. InProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 67–74

    2009

  3. [3]

    Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. InThe world wide web conference. 151–161

    2019

  4. [4]

    Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An ex- perimental comparison of click position-bias models. InProceedings of the 2008 international conference on web search and data mining. 87–94

    2008

  5. [5]

    Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, and Depeng Jin

  6. [6]

    IEEE transactions on knowledge and data engineering33, 2 (2019), 667–681

    Sampler design for bayesian personalized ranking by leveraging view data. IEEE transactions on knowledge and data engineering33, 2 (2019), 667–681

    2019

  7. [7]

    Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, and Fangxi Zhang

  8. [8]

    InProceedings of the AAAI Conference on artificial intelligence, Vol

    A hybrid collaborative filtering model with deep structure for recommender systems. InProceedings of the AAAI Conference on artificial intelligence, Vol. 31

  9. [9]

    Xin Fu. 2010. Towards a model of implicit feedback for web search.Journal of the American Society for Information Science and Technology61, 1 (2010), 30–49

    2010

  10. [10]

    Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, and Baihua Zheng. 2022. Self-guided learning to denoise for robust recommendation. InProceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 1412–1422

    2022

  11. [11]

    Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, and Enhong Chen. 2024. Efficient noise-decoupling for multi-behavior sequential recommendation. InProceedings of the ACM Web Conference 2024. 3297–3306

    2024

  12. [12]

    Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. InProceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 355–364

    2017

  13. [13]

    Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. InProceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648

    2020

  14. [14]

    Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, and Min Zhang. 2024. Double correction frame- work for denoising recommendation. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1062–1072

    2024

  15. [15]

    Katja Hofmann, Fritz Behr, and Filip Radlinski. 2012. On caption bias in inter- leaving experiments. InProceedings of the 21st ACM international conference on Information and knowledge management. 115–124

    2012

  16. [16]

    Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, and Aldo Lipani. 2023. Unlocking the potential of user feedback: Leveraging large language model as user simulators to enhance dialogue system. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3953–3957

    2023

  17. [17]

    Zheng Hu, Zhe Li, Ziyun Jiao, Satoshi Nakagawa, Jiawen Deng, Shimin Cai, Tao Zhou, and Fuji Ren. 2025. Bridging the user-side knowledge gap in knowledge- aware recommendations with large language models. InProceedings of the AAAI Conference on Artificial Intelligence. 11799–11807

    2025

  18. [18]

    Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi, and Christoph Trat- tner. 2024. A survey on popularity bias in recommender systems.User Modeling and User-Adapted Interaction34, 5 (2024), 1777–1834

    2024

  19. [19]

    Tommaso Di Noia, Vito Claudio Ostuni, Paolo Tomeo, and Eugenio Di Sciascio

  20. [20]

    Sprank: Semantic path-based ranking for top-n recommendations using linked open data.ACM Transactions on Intelligent Systems and Technology (TIST) 8, 1 (2016), 1–34

    2016

  21. [21]

    Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, and Yong Li. 2023. Robust preference-guided denoising for graph based social recommendation. In Proceedings of the ACM web conference 2023. 1097–1108

    2023

  22. [22]

    Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, and Niko Suenderhauf. 2023. Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning.arXiv preprint arXiv:2307.06135(2023)

    work page arXiv 2023

  23. [23]

    Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, and Chao Huang. 2023. Disen- tangled contrastive collaborative filtering. InProceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 1137– 1146

    2023

  24. [24]

    Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme

  25. [25]

    BPR: Bayesian personalized ranking from implicit feedback.arXiv preprint arXiv:1205.2618(2012)

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  26. [26]

    Usha Ruby, Vamsidhar Yendapalli, et al. 2020. Binary cross entropy with deep learning technique for image classification.Int. J. Adv. Trends Comput. Sci. Eng9, 10 (2020)

    2020

  27. [27]

    Youchen Sun, Zhu Sun, Yingpeng Du, Jie Zhang, and Yew Soon Ong. 2025. Model- agnostic social network refinement with diffusion models for robust social rec- ommendation. InProceedings of the ACM on Web Conference 2025. 370–378

    2025

  28. [28]

    Yatong Sun, Bin Wang, Zhu Sun, and Xiaochun Yang. 2021. Does Every Data In- stance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data.. InIJCAI. 1579–1585

    2021

  29. [29]

    Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, and Chenghu Zhou. 2024. EditKG: Editing knowledge graph for recommendation. InProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 112–122

    2024

  30. [30]

    Lei Wang, Jingsen Zhang, Hao Yang, Zhi-Yuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Hao Sun, Ruihua Song, et al. 2025. User behavior simulation with large language model-based agents.ACM Transactions on Information Systems43, 2 (2025), 1–37

    2025

  31. [31]

    Shuyao Wang, Zhi Zheng, Yongduo Sui, and Hui Xiong. 2025. Unleashing the Power of Large Language Model for Denoising Recommendation. InProceedings of the ACM on Web Conference 2025. 252–263

    2025

  32. [32]

    Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021. Denoising implicit feedback for recommendation. InProceedings of the 14th ACM international conference on web search and data mining. 373–381

    2021

  33. [33]

    Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou

  34. [34]

    Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems33 (2020), 5776–5788

    2020

  35. [35]

    Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, and Yingzhen Yang. 2024. Recmind: Large language model powered agent for recommendation. InFindings of the Association for Computational Linguistics: NAACL 2024. 4351–4364

    2024

  36. [36]

    Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M Jose, Fuli Feng, and Xiangnan He. 2022. Learning robust recommenders through cross-model agreement. In Proceedings of the ACM web conference 2022. 2015–2025

    2022

  37. [37]

    Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, and Hongzhi Yin

  38. [38]

    InProceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining

    Efficient bi-level optimization for recommendation denoising. InProceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 2502–2511

  39. [39]

    Zongwei Wang, Min Gao, Junliang Yu, Yupeng Hou, Shazia Sadiq, and Hongzhi Yin. 2025. RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents.arXiv preprint arXiv:2503.23374(2025)

    work page arXiv 2025

  40. [40]

    Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, and Min Zhang. 2024. Macrec: A multi-agent collaboration framework for recommendation. InProceed- ings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2760–2764

    2024

  41. [41]

    Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M Jose, Maarten de Rijke, et al . 2023. Improving implicit feedback-based recommendation through multi-behavior alignment. InProceedings of the 46th international ACM SIGIR conference on research and development in information retrieval. 932–941

    2023

  42. [42]

    An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, and Tat-Seng Chua. 2024. On generative agents in recommendation. InProceedings of the 47th international ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation Conference’17, July 2017, Washington, DC, USA ACM SIGIR conference on research and development in Information Ret...

    2024

  43. [43]

    Shuai Zhang, Hua Chu, Jianan Li, Yangtao Zhou, Shirong Wang, and Qiaofei Sun. 2025. DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation. InProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining. 521–529

    2025

  44. [44]

    Yu Zhang, Shutong Qiao, Jiaqi Zhang, Tzu-Heng Lin, Chen Gao, and Yong Li

  45. [45]

    A survey of large language model empowered agents for recommenda- tion and search: Towards next-generation information retrieval.arXiv preprint arXiv:2503.05659(2025)

    work page arXiv 2025

  46. [46]

    Qian Zhao, Shuo Chang, F Maxwell Harper, and Joseph A Konstan. 2016. Gaze prediction for recommender systems. InProceedings of the 10th ACM Conference on Recommender Systems. 131–138

    2016

  47. [47]

    near-miss

    Xinjun Zhu, Yuntao Du, Yuren Mao, Lu Chen, Yujia Hu, and Yunjun Gao. 2023. Knowledge-refined denoising network for robust recommendation. InProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 362–371. A Appendix A.1 User Profiling Prompt An example prompt used to initialize the agent with a deep...

    2023