Unbiased Rectification for Sequential Recommender Systems Under Fake Orders

Cheng Wang; Haozhao Wang; Qiyu Qin; Ruixuan Li; Rui Zhang; Yichen Li

arxiv: 2604.08550 · v1 · submitted 2026-01-24 · 💻 cs.IR · cs.AI

Unbiased Rectification for Sequential Recommender Systems Under Fake Orders

Qiyu Qin , Yichen Li , Haozhao Wang , Cheng Wang , Rui Zhang , Ruixuan Li This is my paper

Pith reviewed 2026-05-16 11:21 UTC · model grok-4.3

classification 💻 cs.IR cs.AI

keywords sequential recommender systemsfake ordersunbiased rectificationdual-view detectiongradient ascentdata manipulationrecommendation systemsbias removal

0 comments

The pith

Dual-view identification enables targeted rectification of harmful fake orders in sequential recommender systems without retraining.

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

The paper shows that fake orders embedded in user sequences are not always detrimental and can sometimes enhance data. It introduces a method to obtain differentiated representations from collaborative and semantic perspectives to detect suspicious orders, then selects only the truly harmful ones for rectification using gradient ascent. This targeted approach removes bias while retaining useful information and preserving the original sequence structure and data volume. Readers would care because it offers an efficient way to defend recommendation systems against manipulation attempts like click farming without the expense of full model retraining.

Core claim

The central claim is that fake orders can be precisely identified as harmful or not using dual-view representations, allowing for targeted gradient-ascent rectification on the harmful subset alone. This achieves unbiased rectification of compromised sequential recommender systems while avoiding the computational costs of retraining, maintaining data volume, and preventing the removal of useful augmentation effects from partial fake orders.

What carries the argument

Dual-view Identification and Targeted Rectification (DITaR), which uses differentiated collaborative and semantic representations to filter suspicious fake orders and applies selective gradient ascent for rectification.

If this is right

Outperforms state-of-the-art methods in recommendation quality on three datasets.
Achieves better computational efficiency by avoiding full retraining.
Enhances system robustness against fake order manipulations.
Preserves useful data augmentation effects while eliminating bias residue.

Where Pith is reading between the lines

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

Applying similar dual-view detection could help address other forms of data poisoning in recommendation systems.
Testing the method on streaming data with evolving fake order tactics would reveal its adaptability.
The gradient ascent rectification step might be combined with differential privacy techniques for stronger guarantees.
Extending the semantic view to include multimodal data like images or text could improve detection in richer datasets.

Load-bearing premise

Differentiated representations from the collaborative and semantic views can accurately distinguish harmful fake orders from useful ones, allowing gradient ascent on the harmful subset to correct bias without creating new distortions.

What would settle it

A controlled test on a dataset with explicitly labeled harmful and useful fake orders where applying DITaR results in no improvement or a decline in recommendation metrics such as NDCG or HR compared to the unrectified model.

Figures

Figures reproduced from arXiv: 2604.08550 by Cheng Wang, Haozhao Wang, Qiyu Qin, Ruixuan Li, Rui Zhang, Yichen Li.

**Figure 2.** Figure 2: The framework of DITaR. DITaR first constructs collaborative and semantic view models to derive dual-view repre [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Fake order detection performance of two sequen [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

read the original abstract

Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactions, including click farming, context-irrelevant substitutions, and sequential perturbations. Unlike injecting carefully designed fake users to influence recommendation performance, fake orders embedded within genuine user sequences aim to disrupt user preferences and mislead recommendation results, thereby manipulating exposure rates of specific items to gain competitive advantages. To protect users' authentic interest preferences and eliminate misleading information, this paper aims to perform precise and efficient rectification on compromised sequential recommender systems while avoiding the enormous computational and time costs of retraining existing models. Specifically, we identify that fake orders are not absolutely harmful - in certain cases, partial fake orders can even have a data augmentation effect. Based on this insight, we propose Dual-view Identification and Targeted Rectification (DITaR), which primarily identifies harmful samples to achieve unbiased rectification of the system. The core idea of this method is to obtain differentiated representations from collaborative and semantic views for precise detection, and then filters detected suspicious fake orders to select truly harmful ones for targeted rectification with gradient ascent. This ensures that useful information in fake orders is not removed while preventing bias residue. Moreover, it maintains the original data volume and sequence structure, thus protecting system performance and trustworthiness to achieve optimal unbiased rectification. Extensive experiments on three datasets demonstrate that DITaR achieves superior performance compared to state-of-the-art methods in terms of recommendation quality, computational efficiency, and system robustness.

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.

Desk Editor's Note private letter to a colleague

DITaR gives a practical pipeline for spotting and fixing harmful fake orders in sequential recommenders without full retraining, but the dual-view detector's accuracy is not shown in enough detail to trust the separation.

read the letter

The paper's main contribution is DITaR, which uses collaborative and semantic views to flag suspicious fake orders, keeps only the truly harmful ones, and applies gradient ascent just to those for correction. It explicitly notes that some partial fakes can help as augmentation, so the method tries to avoid blanket removal. That framing is sensible for real e-commerce sequences where click farming mixes with normal behavior. The approach also preserves original data volume and sequence structure, which matters when retraining is too expensive. Experiments on three datasets report gains in recommendation quality, efficiency, and robustness over prior methods, which is the kind of end-to-end result practitioners care about. The no-retrain angle is a clear practical plus. The soft spot is the identification step. The abstract gives no precision or recall numbers for the dual-view filter, no ablation that isolates how much the two views help, and no check on whether the detector accidentally drops useful partial fakes or leaves residual bias. Without those, it is hard to know if the rectification is truly unbiased or just shifting the problem. The circularity risk is real too: the same representations used to define harm are later corrected by the model. A reader working on production sequential recommenders would find the pipeline worth trying, especially if they already have labeled fake-order data to tune the detector. The work is concrete enough and the problem timely enough that it deserves a serious referee, mainly to pressure the authors on validation of the filtering step and to add the missing ablations and error analysis. I would send it out rather than desk reject.

Referee Report

3 major / 2 minor

Summary. The paper proposes Dual-view Identification and Targeted Rectification (DITaR) to address fake orders in sequential recommender systems. It uses differentiated representations from collaborative and semantic views to identify harmful samples (noting that partial fake orders may augment data), then applies targeted gradient-ascent rectification on the harmful subset to achieve unbiased correction while preserving sequence structure and data volume, avoiding full model retraining. Experiments on three datasets claim superior performance over state-of-the-art methods in recommendation quality, computational efficiency, and robustness.

Significance. If the dual-view separation reliably distinguishes harmful from useful fake orders and the rectification step removes bias without introducing new distortions, the approach could offer an efficient, low-cost alternative to retraining for handling manipulated interactions in production sequential recommenders. The insight that fake orders are conditionally harmful is a useful contribution, but the absence of detailed validation metrics and ablations in the core identification and rectification pipeline limits the assessed significance.

major comments (3)

[DITaR method description] The dual-view identification of harmful samples (collaborative vs. semantic representations) is load-bearing for the unbiased claim, yet no precision/recall on labeled harmful subsets, no ablation isolating the dual-view contribution, and no analysis of misclassification risk for augmentation-useful samples are provided; this directly affects whether residual bias remains or useful signal is discarded.
[Targeted rectification step] The targeted gradient-ascent rectification on the filtered harmful subset is asserted to remove bias without new distortions or sequence-structure damage, but no empirical checks (e.g., sequence-length statistics, embedding-shift analysis, or comparison of pre/post-rectification preference distributions) are reported to substantiate this.
[Experiments section] Experimental claims of superiority lack error bars, specific baseline implementations, statistical significance tests, and details on how the central identification step isolates harmful samples; the abstract and results sections provide only aggregate performance numbers, preventing verification of robustness.

minor comments (2)

[Method] Notation for the dual-view representations and the gradient-ascent update rule should be formalized with equations to clarify the exact filtering and rectification operations.
[Introduction] Related-work discussion on fake-order detection and data-augmentation effects in sequential recommenders could be expanded with additional citations for context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight important areas for strengthening the presentation of DITaR. We agree that additional validation metrics, ablations, and empirical checks will improve clarity and verifiability. Below we respond point-by-point to the major comments and indicate the revisions we will make.

read point-by-point responses

Referee: The dual-view identification of harmful samples (collaborative vs. semantic representations) is load-bearing for the unbiased claim, yet no precision/recall on labeled harmful subsets, no ablation isolating the dual-view contribution, and no analysis of misclassification risk for augmentation-useful samples are provided; this directly affects whether residual bias remains or useful signal is discarded.

Authors: We acknowledge that the manuscript does not report explicit precision/recall figures for the identification module or dedicated ablations separating the dual-view contribution. In the revision we will add these: (i) precision/recall on synthetically labeled harmful subsets constructed from the three datasets, (ii) an ablation table comparing single-view (collaborative-only and semantic-only) versus dual-view identification, and (iii) a misclassification-risk analysis that quantifies the fraction of augmentation-useful samples incorrectly filtered, together with the resulting impact on downstream NDCG. These additions will directly substantiate the claim that the filtering step preserves useful signal while removing bias. revision: yes
Referee: The targeted gradient-ascent rectification on the filtered harmful subset is asserted to remove bias without new distortions or sequence-structure damage, but no empirical checks (e.g., sequence-length statistics, embedding-shift analysis, or comparison of pre/post-rectification preference distributions) are reported to substantiate this.

Authors: We agree that direct empirical verification of the rectification step is currently missing. In the revised manuscript we will include: (i) before/after sequence-length histograms and statistics to confirm structure preservation, (ii) cosine-similarity and L2-norm analyses of item embeddings before and after rectification on a held-out validation set, and (iii) Kolmogorov-Smirnov tests comparing pre- and post-rectification item-preference distributions. These checks will demonstrate that the gradient-ascent updates remove bias without introducing measurable new distortions. revision: yes
Referee: Experimental claims of superiority lack error bars, specific baseline implementations, statistical significance tests, and details on how the central identification step isolates harmful samples; the abstract and results sections provide only aggregate performance numbers, preventing verification of robustness.

Authors: We will expand the experimental section to report mean and standard deviation across five random seeds (displayed as error bars), provide explicit hyper-parameter settings and code references for all baselines, perform paired t-tests with p-values for all reported improvements, and add a dedicated subsection detailing the identification pipeline (including threshold selection and filtering criteria) with illustrative examples from each dataset. These changes will allow readers to verify both the magnitude and statistical reliability of the gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper proposes DITaR using dual-view (collaborative and semantic) representations to identify and selectively rectify harmful fake orders via gradient ascent, while preserving useful augmentation effects. No equations or steps reduce by construction to fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations. The core claim rests on empirical experiments across three datasets rather than an internal equivalence; the identification step is presented as an independent modeling choice, not derived tautologically from the rectification target. This is the common case of a self-contained empirical method.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that fake orders can be partitioned into harmful and useful subsets via dual-view representations, with no free parameters or invented entities declared in the abstract.

axioms (1)

domain assumption Fake orders are not absolutely harmful and partial fake orders can have a data augmentation effect.
Stated explicitly as the key insight enabling selective rather than wholesale removal.

pith-pipeline@v0.9.0 · 5568 in / 1218 out tokens · 47825 ms · 2026-05-16T11:21:01.273708+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

obtain differentiated representations from collaborative and semantic views for precise detection, and then filters detected suspicious fake orders to select truly harmful ones for targeted rectification with gradient ascent
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

influence function to identify truly harmful fake orders... Inf(ik) = -g_v^T H^{-1}_θ ∇_θ L(ik, θ)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · 1 internal anchor

[1]

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Session-based Recommendations with Recurrent Neural Networks

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Machine unlearning of features and labels,

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In2021 IEEE symposium on security and privacy (SP), 141–159

Machine unlearning. In2021 IEEE symposium on security and privacy (SP), 141–159. IEEE. Chang, J.; Gao, C.; Zheng, Y .; Hui, Y .; Niu, Y .; Song, Y .; Jin, D.; and Li, Y . 2021. Sequential recommendation with graph neural networks. InProceedings of the 44th interna- tional ACM SIGIR conference on research and development in information retrieval, 378–387. ...

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Session-based Recommendations with Recurrent Neural Networks

Session-based recommendations with recurrent neural networks.arXiv preprint arXiv:1511.06939. Kang, W.-C.; and McAuley, J. 2018. Self-attentive sequen- tial recommendation. In2018 IEEE international confer- ence on data mining (ICDM), 197–206. IEEE. Kodge, S.; Ravikumar, D.; Saha, G.; and Roy, K. 2025. SAP: Corrective Machine Unlearning with Scaled Activa...

work page internal anchor Pith review Pith/arXiv arXiv 2018

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Machine unlearning of features and labels,

Machine unlearning of features and labels.arXiv preprint arXiv:2108.11577. Wu, C.; Lian, D.; Ge, Y .; Zhu, Z.; and Chen, E. 2021. Triple adversarial learning for influence based poisoning attack in recommender systems. InProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Min- ing, 1830–1840. Wu, C.; Lian, D.; Ge, Y .; Zhu, Z.; and...

work page arXiv 2021