Denoising Neural Reranker for Recommender Systems
Authors: Wenyu Mao
Shuchang Liu
HailanYang
Xiaobei Wang
Xiaoyu Yang
Xu Gao
Xiang Li
Lantao Hu
Han Li
Kun Gai
An Zhang
Xiang Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-stage Recommenders
Reranking Model
Adversarial Learning
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recommender calls a slower but more sophisticated reranking model that refines the item list exposure to the user. To consistently optimize the two-stage retrieval reranking framework, most efforts have focused on learning reranker-aware retrievers. In contrast, there has been limited work on how to achieve a retriever-aware reranker. In this work, we provide evidence that the retriever scores from the previous stage are informative signals that have been underexplored. Specifically, we first empirically show that the reranking task under the two-stage framework is naturally a noise reduction problem on the retriever scores, and theoretically show the limitations of naive utilization techniques of the retriever scores. Following this notion, we derive an adversarial framework DNR that associates the denoising reranker with a carefully designed noise generation module. The resulting DNR solution extends the conventional score error minimization loss with three augmented objectives, including: 1) a denoising objective that aims to denoise the noisy retriever scores to align with the user feedback; 2) an adversarial retriever score generation objective that improves the exploration in the retriever score space; and 3) a distribution regularization term that aims to align the distribution of generated noisy retriever scores with the real ones. We conduct extensive experiments on three public datasets and an industrial recommender system, together with analytical support, to validate the effectiveness of the proposed DNR.

Denoising Neural Reranker for Recommender Systems
DENOISING NEURAL RERANKER FOR RECOMMENDER SYSTEMS
Wenyu Mao Shuchang Li Hailan Yang", Xiaobei Wang', Xiaoyu Yang', Xu Gao?, Xiang Li, Lantao Hu², Han Lif, Kun Gaf,, An Zhang', Xiang Wang'! University of Science and Technology of China Kuaishou Technology
ABSTRACT
For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks list of relevant items, then the recommender calls a slower but more sophisticated reranking model that refines the item list exposure the user. To consistently optimize the two-stage trieval reranking framework, most efforts have focused on learning reranker-aware retrievers. In contrast, there has been limited work on how to achieve a retriever- aware reranker. In this work, we provide evidence that the retriever scores from the previous stage informative signals that have been underexplored. Specifically, we first empirically show that the reranking task under the two-stage framework is naturally noise reduction problem the retriever scores, and theoretically show the limitations of naive utilization techniques of the retriever scores. Following this notion, we derive an adversarial framework DNR that associates the denoising reranker with a carefully designed noise generation module. The resulting DNR solution extends the conventional score error minimization loss with three aug- mented objectives, including: 1) a denoising objective that aims to denoise the noisy retriever scores to align with the user feedback; 2) an adversarial retriever score generation objective that improves the exploration in the retriever score space; and 3) a distribution regularization term that aims to align the distribution of gener- ated noisy retriever scores with the real ones. We conduct extensive experiments on three public datasets and an industrial recommender system, together with analytical support, to validate the effectiveness of the proposed DNR. The code is released at at/https: htttt::://gituu.... com wwtp://www//moommooo/ 11tp/owenyuu11/DDR 11/DNR
INTRODUCTION
Recommender systems aim to generate personalized item lists and maximize users' engagement. For large-scale item pools, industrial solutions Ricci et al. (2022); Liu et (2025) typically use two-stage retriever-reranker architecture. The retriever efficiently narrows down large item pool to manageable relevant candidate set. The reranker then uses a more sophisticated model to reorder the candidates into optimal list-wise order. To optimize the two-stage framework towards consistent goal of recommendation (e.g., aligning with user behavior or preference) across all stages, the majority efforts have been focusing learning a reranker-aware retriever Gallagher al. (2019); Xu et al. (2024)). However, there is limited work on how to achieve a retriever-aware reranker.
reranker/Carbonell & Goldstein 1998): Jin al. (2008) Pei al. 2019); Liu al. (2022). early studies propose to re-score the retrieval items Jin et al. (2008); ; Ai et al. (2018) Pei et al. (2019) by modeling the mutual influences among candidate items. Recent of approaches find it more effective to formulate the reranking task as a list generation problem (Sh et al. (2023);
For the latter reranker Carbonell
Ren et al. (2024): Lin et al.) (2024), where the lists with better list-wise rewards are given higher generation probability. Yet, existing methods largely overlook the potential of initial retriever scores, which may offer rich prior information for the reranker stage. In practice, a straightforward approach
"Wor done during the internship at Kuaishou: wenyumao2 @ereeigmmallooooomm 1 soonesnnniing author: liushuchang @ kuaishou.com. xiangwang @ uustc.du.cc The first authors contributed equally this work

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Denoising Neural Reranker for Recommender Systems
to leverage initial retriever scores is to directly include them extra input features of the model, which shows promise to improve reranking performance as in Figure(Ia) Still, as we will illustrate in section 3.2 this naive solution might be far from exploiting this retriver information effectively to align with user feedback, which is addressed by the following contributions in this work.
The retriever stage typically employs a simpler model than the reranker to process large candidate pools, given computational budget constraints. In contrast, the reranker, operating on a much smaller candidate set, can use a more sophisticated architecture and thus achieves significantly higher accuracy (Figure a lb). What follows is an empirical assumption that there is higher noise the retriever scores than the reranker scores, which naturally indicates a noise reduction process of the reranking stage. This noise reduction pattern also evident when we observe the changes in the embedding representations of ground truth target items. We show evidence by comparing Figure [c]and Figure [d] where the same set of retrieved candidates not distinguished well by the retriever, but the target items are better predicted by the reranker, in terms of the embedding space. The reranker's noise is more concentrated near zero than the retriever's 's (Figure Ie]. further confirming the reranker acts as a denoising model for the retriever's 's noise. However, due to the uncertainty inherent in noisy retriever scores, denoising based on these scores may misalign with the system- -ystem-wide wide goal of aligning with user behavior. Thus, we argue that learning a noise-aware reranker is crucial for achieving retriever-aware reranker.
Formulating reranking
noise reduction problem:
NDCG810
Useful Score Information
0.75
0.70
NAH
ABC
0.65
Reizzer
0.60
Robriver
Heronier Reranier w/'
(a) Initial retriever scores fur- ther boost the performance.
(b) Performance comparison of two stages in Movicl.ens datasct.
Embadding in Retriever Stage
Embadding in Reranker Stage
Noise Distribution Comparison
20
5-
Reaankr
4-
0.5
3-
0.4
2-
-10
Ratriover
forethor
-20
Targe!
Tagel
-10
0.20
0.25
0.50
0.75
1.00
-1.0
-0.5
0.0
0.5
(c) Target item and selected can- didates embedding in retriever.
(d) Noise reduction process in item embedding perspective.
(e) Noise density distribution of the reranker and retriever.
Figure 1: (a) The informative retriever score and (b-d) noise reduction nature of reranking. The reranker in this example uses transformer model to select the top-20 items from the candidates. "Rerank w/ s" represents using retriever scores additional item features for the reranker. The black shaded circles in (c) and (d) represent the retriever's 's selection of candidate items, and the green shaded circle in (d) represents those of the reranker for exposure. (e) compares the noise distribution (distance between predicted scores and ground-trude labels) of the retriever and the reranker.
The proposed solution: Motivated by the previous analysis, we propose an adversarial framework. which formulates a denoising neural reranker (DNR) with carefully designed noise generation module, which introduces unseen noise to users' feedback to augment the distribution modeling of retriever scores. We theoretically show that the user behavior alignment goal can be decomposed into three learning objectives in our solution framework: 1) denoising objective that aims to denoise the noisy retriever scores under both observed and synthetic retriever scores; 2) an adversarial objective that encourages the noise generator synthesize adversarial samples that are hard to denoise, which in turn improves the reranker's s effectiveness; and 3) a score distribution regularization term that aims to align the distribution between synthetic retriever scores with the real retriever scores.
We conduct empirical experiments on three public datasets and an industrial recommender system to verify the superiority of DNR against naive utilization strategies of retriever scores, well as leading
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Denoising Neural Reranker for Recommender Systems
reranker models. Additionally, find that the three proposed learning objectives in the adversarial denoising framework yield better recommendation performance than single denoising objective.
RELATED WORK
2.1 MULTI-STAGE RECOMMENDATION
Modern content-intensive web services with large candidate pools typically employ multi-stage recommender systems Qin et al. (2022); Zheng et al. (2024); Liu et al. (2022) (also known cascade ranking Liu et al. (2017): Gallagher et al. (2019) process) to balance accuracy and efficiency under strict latency constraints. Each stage progressively narrows down the candidate set while employing increasingly sophisticated models. While most existing work focuses on independently optimizing Wang individual stages (e.g., retrieval Wang et al. 2011 2017) or the reranking stage [iu et al. (0022);
Pei et all.]22011 (2019)) ) recent studies explore joint optimization across the entire pipeline Gaalagher et al. (2019); Qin al. 2022); Xu et al. (2024b). These approaches leverage later-stage models to guide carlier-stage learning, improving overall coherence and performance Zhao et al. (2023); Xu et al. (2024a). In contrast, our work takes a complementary direction usiig earlier-stage scores to regulate later-stage optimization.
2.2 RERANKING STRATEGIES
In sequential recommendation. re-ranking Pei et al. (2019); Huzhang et al. (2023); Ai et al. (2018) aims to refine the initial ranking list generated by a previous retrieval stage. Existing re-ranking approaches can be broadly categorized into the following paradigms. Single-poin point re- re-scoring methods Kang & McAuley 2018); ; [Zhuang et al. (2018) independently predict refined score for each item and re-rank them accordingly. While efficient, they ignore dependencies between items, potentially leading suboptimal list-level performance. Unlike single-point approaches, list-refinement methods Pei et al. (2019): Ai et al.] (2018); Xi et al. (2022) explicitly model mutual influences between items by treating the pre-ranked candidate list as input. Techniques such as Transformer- architectures Pei et al. (2019) capture user preferences to optimize the list holistically. Generator- evaluator methods HHuzhagg and then evaluate them using a learned utility function to select the best one. They can effectively generate high-quality recommendation list but suffer from the increasing computational cost due to the generation- generation-evaluation loop. Emerging techniques leverage diffusion models Mao al. (2025c d) to generate refined item lists by iteratively denoising a ranking distribution, which excels in capturing complex user preferences.
al. (2023): Shi et al., (2023) first generate multiple candidate lists
2.3 ADVERSARIAL LEARNING
Adversarial learning Lowd & Meek 2005) has emerged as a powerful technique for data augmentation in recommendation systems, improving the model robustness and generalization. By leveraging generative adversarial networks (GANs) Wang et al. (2017) or adversarial training He et al. (2018): Tang et al. (2020), these methods synthesize high-quality user-item interactions perturb existing data to enhance model robustness. For instance, IRGAN WWnn et al. (2017) employs a minimax game between a generator (sampling hard negative items) and a discriminator (distinguishing real from synthetic interactions) to improve ranking performance. Similarly, AdvIR Park & Chang (2019) introduces adversarial perturbations to embedding spaces, forcing the model to learn more generalizable representations. These methods demonstrate that adversarial learning can not only augment training data but also enhance models' robustness. Our method is designed to add noise by adversarial learning, which can enhance the reranker's 's effectiveness in denoising the pre-ranking scores to align with user feedback.
METHODOLOGY
3.1 PROBLEM FORMULATION
For a given user request u (which may contain information about the candidate items I(u) = (i,, i2, , retrieved by the retriever, user profile features, and interaction history),
3

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Denoising Neural Reranker for Recommender Systems
Display
bb 186-
Tens
user feedback after display Zu
Adversarial Learning
Reranker
Denoising
Noise Generator f
ail
Hundreds
Reranker
Ladv
multi stage
Posterier Ropalettathes
Retriever
Thousands
item list I Retriever results
score list Xy
Item Set
Millions
Noise Reduction Formulation Reranker
Figure Overall framework of multi- stage recommender system (on the left) and the noise reduction formulation of our method, DNR.
observe the following during data collection: 1) The corresponding (continuous) retriever scores of Tx,,] [0, 1]", which essentially describe the user preference prediction in the view of the retriever; 2) The ground truth user feedback (i.e., binary labels such as click, watch, 1. E {0, 10. Note that for recommender systems that further select K items after the reranking process, we may never observe the positive user feedback for the remaining labels as defaults for these items, since passing the reranking process is the premise of observing the user feedback signals.
[u) are X = 1
share, etc.) of the candidate set, i.e., Z, [Zi, Zig. 2r
K items. From probabilistie viewpoint, we can assume zero
The Noise Reduction Task: In formulation, the overall recommendation-fecdee + x + (where x and z are random variables for retriever scores and user feedback) involves an initial retriever Px (x(()),, which serves the retriever score generation prior (and is assumed non-optimizable in the reranking task). The learnable reranker go (u(z,, u) serves as the conditional user feedback likelihood estimator that predicts the feedback á given the retriever scores and the user context u. i.e., & ~ qe(-|xu, u1. Given an observed feedback Zn the learning goal adopts the general data log likelihood maximization objective:
process
max log p (z.. u, 0)
where the predicted likelihood p(zu lu, 0) for the observed user feedback z, is determined by the retriever Px, the reranker qo, and the user request u.
For simplicity of the presentation, we omit the user condition term u for the remainder of the paper (e.g., we will denote the retriever Px (xx),, the reranker as qe (z) xzxx), and simplify the learning goal as maxy log p(z.)), since we adopt an analysis framework on the user request level.
Note:
3.2 ANALYTICAL LIMITATION OF DIRECT OPTIMIZATION METHODS
As we have mentioned in section I involving retrieval scores as input is beneficial for existing reranking methods. Regardless of the reranker design, the corresponding objective function is:
Ldirect Exxop, [log qe (z.. [x)[
which maximizes the user behavior alignment conditioned the given retrieval scores. As special case, according to the binary nature of z,, we can simplify the learning objective as standard binary cross-entropy (BCE) loss. Theoretically, we can find that Lairett optimizes an upper bound of the negative data log likelihood (details in Appendix B.2):
- log p(z.)) = Ldirect + To + T.2
log 2o (o xx) Para (z)
In = Exxps
(3)
Lg = DKL (xx)
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Denoising Neural Reranker for Recommender Systems
where Pax represents the ground- truth user feedback probability and Pxu represents the posterior distribution of retriever scores conditioned on user feedback, both terms are not related to the reranker qo, and they merely determined by the retriever prior and the user. Intuitively, smaller values of L1 and Iz2 make the solution of Eq. (2) more aligned with the goal of Eq.(T). However, these two terms not fully controlled by the direct optimization framework, potentially causing significant
misalignment between Eq. ( and [. This will lead to discrepancy between the final reranking results and user feedback, and thus, the suboptimal recommendation performance.
3.3 IMPLEMENTING NOISE GENERATOR FOR THE POSTERIOR
To overcome the aforementioned limitations, propose to complement the denoising reranker qe with a noise generator fo(-)zu). Formally, we generate noise with ~ fo and add it to the user feedback form the synthetic retriever score posterior P& px(x)zu), taking insight from the reparameterization trick Kingma & Welling (2014):
x, = (1 Ac)zy + Xge,
where the generated noise e represents the noisy behavior in retriever scores x de represents the proportion of noise in the generated retriever scores. We denote the generation process x,, Pa- which analogous to the sampling process with ground truth posterior (assumed unknown and Pxtz- For implementation, investigate choices of noise generator sg:
intractable), i.e.,
Heuristic Generator: This option uses a non-parametric noise generator pheuristic with a manually designed distribution. For example, we can adopt random Gaussian noise similar to Ho et al. (2020); et et al. (2024). but we notice that this is not good representation of the noise in retriever scores. Intuitively, given the binary user feedback signals, natural assumption of the retriever prior the Beta distribution, i.e., the conjugate prior and the posterior for binary variables (i.e., bernoulli or binomial distribution) are both beta distribution, where we can derive the heuristic solution Beta(a, 3). For simplicity, one may set B 0.5 to approximate the uncertainty. In practice, we find that it is also beneficial to set a and 3 that align with the observed noise distribution in the data.
Model-based Generator: While the heuristic generator is effective in our empirical study, we are skeptical of whether the manually designed heuristic noise generator is optimal for augmenting the retriever score distribution. Since the prior noise distribution is uncertain, it is necessary to learn this distribution adaptively and personally. To achieve a more precise estimation, we design a learnable model /model with parameter é, that will be optimized along with the reranker (details in the next section). The generated noise should also be aligned with the observed distribution, and it also participates an adversarial training objective in order to better explore the retriever score space, which in turn improves the reranker's effectiveness. As the noise is learned, A requires no manual adjustment and can be treated as 1. As we will show in Section 4.3 this choice is empirically better than the heuristic generator.
3.4 OVERALL LEARNING FRAMEWORK
In this section, show how optimize the user feedback alignment goal, i.e., Eq. by simulta- neously learning the denoising reranker and the adversarial noise generator. Specifically, we can decompose the negative log- likellhoooo objective into three loss terms (derivation in Appendix B.T):
log p(zu) L, Ludy La 8x
L, -Exups
log 9o (Zn) x
Ladv Exxupo log To (z,lx)) (x)
(5)
Peta Zu x)
C, DKL (x)(x(z (xxx
which consists of augmented denoising loss term Lz. adversarial noise generation loss Ladv- and a score distribution regularization term L.. We will illustrate the details of these three terms in the following sections. The additional term & - DDL (x(2)),,, Pl k2.)) S 0 represents
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Denoising Neural Reranker for Recommender Systems
the negative divergence between the generated noisy retriever scores ps and the corresponding real Pxtz- This term is not directly optimizable due to the unknown Paiz, but we expect to achieve a small divergence in 6₂ with the optimization of the other three terms, so that the overall optimization is aligned with the goal of Eq. O..
Denoising with Augmented Retriever Scores: C, in Eq.(5) (5) denotes the denoising loss under the noisy retriever scores generated by the noise generator. Specifically, we can first synthesize X ~ (lz() as the generated retriever scores according to (4) then pass them into the reranker qe for the prediction of user feedback Zu, and L, only updates 0. The optimization can also be simplified into the BCE loss with zu as labels. Note that the generated scores x,, different from the observed x essentially serve as the augmented scores that the initial retriever would have produced given the user request. This means that the reranker will be aware of the noisy behavior of the retriever. Combining with the direct optimization, we have the final denoising loss for the reranker:
Lo Lairect + Xm Ln
(6)
where Am controls the magnitude of the augmentation, and the parameters of the noise generator fa are fixed. Additionally, our model-agnostic framework does not restrict the architecture of the denoising reranker qa. we will show in section 4.3 one can adopt various backbones, including
PRM Pei et al. (219)) and PIER Shi et al. (003))
Adversarial Noise Learning: fanv describes how the reranker qo behaves differently from the user feedback under the viewpoint of the synthetic retriever scores. In practice, this term is expected to be small since the ratio (z,, x)) //a/ (2. (Z., (x) se 1 in major cases due to the user feedback alignment of (6 and the augmented objective L. Nevertheless, in the view of generated noisy retriever scores, we can see that Ladv encourages adversarial samples of that produce small ratios of go (o(2alx)/Paa Pax (zu (x). In other words, a better p& should be able to explore and synthesize retriever scores that are incorrectly denoised by the reranker qo. For the heuristic noise generator fheuristic, is not optimizable once a and are chosen. In contrast, for the model-based generator fmodee we follow the intuition of adversarial case exploration, and approximate Lavv by first fixing the reranker and then optimizing the generator with an adversarial loss:
min log go xx).
We implement the model-based generator fmodde by a 2-layer MLP and directly output x', using Eq.44. so that the end-to-end learning becomes feasible for 77. In cases where the noise generator not directly optimizable, one may have to consider other alternatives like reparameterizatioo tricks Kingma & Welling 2014) or policy gradient [Silver et al. (2014). Notably, Lady does not encourage over-exploration of x which may negatively impact the learning of the reranker. In such cases, the synthetic scores are unrealistically different from the real retriever scores, diverging from the user feedback, inducing small pzix and large qo (z., (x..Pxx(z...x)) violating Ladv.
Noise Regularization: L, aims to reduce the divergence between the synthetic retriever score distribution pe and the real retriever score prior distribution Px. Intuitively, this term favors the synthetic score distribution Ps that can mimic the noisy behavior of the real retriever's ' scores Px. For implementation, we first synthesize the retriever score x,, with (4. and calculate the distributional difference between synthetic scores from Ps and retriever scores from p.. Then, we minimize this difference to approximate the distribution- KI. divergence.
Again, given that the heuristic noise generator f pheuristic is not optimizable by the objectives in Eq 5 it might not be the optimal choice for augmenting the retriever score distribution. As a consequence, this could lead to recommendations that are not aligmment with user feedback. For the training of the model- noise generator f!nodel, we first adopt the heuristic generator in the first X epochs to ensure learning stability of reranker throughout L. then engage the adversarial learning of Ludv and the optimization of L, afterward, switching the generation of from heuristic generator to model- based generator. We present the algorithm of our training procedure in Appendix C
4 EXPERIMENTS
In this section, we conduct extensive experiments answer the following questions:

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Denoising Neural Reranker for Recommender Systems
RQI: How does DNR perform compared with leading rerankers in recommendation systems?
RQ2: How does the denoising formulation compare to other methods of using retriever scores?
RQ3: Can the denoising formulation generalize reranker backbones other than PRM?
RQ4: How does the heuristic noise generator's performance compare to that of the adversarial model-based generator?
RQ5: How does different objectives in DNR contribute to the performance?
RQ6: How sensitive is DNR to the hyperparameters Ao, Am. and Ac?
4.1 EXPERMENTAL SETTINGS
Datasets.
We conduct experiments on three real-world datasets, including ML-IM Harper & Konstan
(2016), Kuaivideo Liu al. (2025), and Amazon-books Ni et al. (2019). The detailed description and the statistics of the processed datasets for both the retriever and the reranker stages are presented in Appendix D.I For the online experimental verification, please refer to Appendix [.66
We compare the performance of DNR with four categories of recommendation methods in our reranking task, including traditional recommenders (i.e., SASRec Kang & McAuley (2018).
Baselines:
Caser TTag & Wang (2018), GRU4Rec Hidasi et al. (2016), and MiDNN ZZhang et al. (2018)) which individually predict the scores of candidate items and rank them accordingly; list-refinement methods (i.e. DLCM Ai et al. (2.18) SetRank Pang et al. (202)). PRM PPi et al. (2019). 19. and MIR Xi et al. (2022). which encode the mutual inference in candidate items and refine the rankings based on this information; generator-evaluate
methods (i.e., EGRerank Huzhang et al. (023). Pier Shi ct al. (2023). and NAR4Rec Ren et al. (2024)), which generate multiple reranked item lists and evaluate them to select the best one for users; and diffusion-Based methods (i.e.. DiffuRee Li ct al.
(2024) and DCDR Lin et al. (2024)) which utilize diffusion models to generate items or lists for recommendation. Detailed information for baselines is presented in Appendix D.3
Our experiments are implemented with Python 3.8 and PyTorch 1.12. We construct the retriever stage with the collaborative filtering method Koren et al. (2009) obtain the initial retriever scores for the top-50 candidate items. The detailed setting and experimental results of the retriever stage presented in Appendix E. E. The reranker reorders these items filtered by retrievers and selects the top K = 6 for display. The dimension of rerankers' hidden embedding is 128 across all models, and all models are trained until convergence. The learning rate and weight decay are tuned within the range of [0.01, 0.001, 0.0001] and [0, 0.1, 0.01, 0.001], respectively. We tune the hyperparameters with A E [0.1, 1.0), ,, Am E [0.1, 1. 1.0), and X E [0, 200). The optimal hyperpapemters adopted for our experimental results are presented in Appendix D.2
Implementation Details:
Evaluation Metrics:
Following common practices, we employ Hit Ratio@6 (H@6), NDCG@6 (N@6), MAP@6 (M M6)), Fl @6, and AUC for performance evaluation of the reranker stage. Although not the focus of this paper, we adopt the AUC metric to evaluate the performance of the retriever stage. And the retriever is fixed during training of rerankers.
4.2 MAIN RESULTS (RQ1)
We compare the performance of our proposed DNR with multiple leading baselines and the results are detailed in Table 1 "DNR-G" and "DNR-B" denote using two alternatives of f fheuristic generate Gaussian noise and Beta noise, respectively, before switching to the model-based noise generator frundel in the epoch A.. We can observe that the generator evaluator-basee methods (i.e., EGRerank Huzhang et al. (2023), Pier Shi et al. (2023). and NAR4Rec Ren et al. (2024) achieve comparable performance among the multiple baselines, validating the effectiveness of the generator-evaluatoo paradigm in the reranker stage. state-of-the-an baselines across the three benchmarks. This highlights the superiority of our noise reduction formulation and the significance of improving reranker effectiveness with three objectives in adversarial learning.
comparison, our DNR solutions consistently outperform the
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Denoising Neural Reranker for Recommender Systems
Table I: Overall performance of different methods for the reranking recommendation. The best scores are in bold and the best baseline scores are underlined, respectively. All improvements are statistically significant with student t-test p < 0.05.
ML-IM
Kuaivideo
Book
Methods
H@6
N@6
M@6
F1@6
AUC
H@6
N@6
M@6
F1@6
ALC
H@6
N@6
M@6
F1@6
AUC
SASRec
59.79
72.16
61.84
65.41
85.98
36.72
54.01
41.66
43.00
77.86
60.27
69.32
58.17
62.44
83.64
Caser
58.60
71.34
60.55
64. 14
86.53
36.66
54.04
41.65
43.93
78.39
59.92
69.31
58.06
62.09
84.21
GRU4Rec
58.07
69.91
59.03
63,47
86.74
37.28
54.39
42.20
43.66
74.67
53.69
59.24
46.71
55.65
81.19
MiDNN
56.86
70.30
59.28
62,16
86.87
37,32
54,56
42.38
43.72
74.73
60.28
69.61
58.58
62.45
83.02
DLCM
62.31
73.87
63.82
67.96
89.35
39.69
60.67
48.90
46.61
75.80
66.80
75.88
65.39
69.28
91.93
SetRank
59.35
73.10
62.51
64.72
88.84
44.75
64.39
$1.88
52.59
89.93
66.15
75.70
64.84
68.64
92.01
PRM
60.09
72.85
62.21
65.51
88.20
39.92
55.93
42.97
46.18
85.15
67.86
76.88
66.44
70.42
92.00
MIR
62.22
74.33
64.47
67.97
87.76
37,01
55.79
43.16
44.50
79.95
66.08
71.48
56.62
68.62
91.82
EGRank
62.76
74.75
64.97
68.46
88.72
40.09
59.01
47.52
47.06
77.44
70.73
80.75
72.40
73.33
89.40
Pier
62.74
75.99
65.98
68.74
90.43
45.35
65.11
52.55
53.35
90.93
71.14
80.22
71.62
73.74
92.26
NAR4Rcc
62.81
75.01
65.42
68.31
88.30
44.31
63.83
51.45
52.08
89.94
70.08
79.46
70.69
72.66
92.44
MG-F
63.53
16.18
66.52
69.37
89.08
46.89
66.19
53.75
55.26
90.16
70.63
79.81
71.07
73.20
90.21
DiffuRec
62.16
75.80
66.28
67,83
91.48
40.78
61.16
50.77
48.00
81.08
58.62
68.77
57.25
60.79
84.74
DCDR 60.79
73.21
62.57
66.58
89.01
43.86
63.52
50.92
51.66
89.92
65.84
70.36
54.76
67.33
88.07
DNR-G 64.891
77.671
68.001
70.711
92.751
4
69.601
57.671
58.391
93.201
74.831
82.571
74.24
77.651
94.461
DNR-B
64.231
77.12
67.301
70.01
92.08
50.30"
70.151
58.121
59.161
93.381
75.501
83.531
75.541
78.301
94.231
4.3 ABLATION STUDY (RQ2, RQ3, RQ4, & RQ5)
RQ2& & RQ3: To investigate different ways to leverage retriever scores, we develop three variants for leveraging retriever scores as rerankers' input: "c score" refers to leveraging a concatenate operation to combine the score features with encoded user states from users' interaction history; "+ score" denotes incorporating the score feature with user states by a simple addition; "w score" represents adopting the initial retriever scores as the weights of candidate items. The frameworks of the three variants are shown in Appendix D.4 To validate the generalizability of DNR across different reranker backbones, we conduct experiments comparing our three variants and DNR itself when using PRM |Pi et al. (2019) and Pier et al. (2023) the underlying rerankers, respectively. We report the experimental results in Table The variants "c score", "+ score", and "w score" all achieve superior performance compared to the base reranker backbone, confirming that incorporating retriever scores into the reranking stage yields tangible benefits. Morcover, our DNR outperforms all three variants, underscoring denoising formulation as a more effective strategy for leveraging retriever scores. Importantly, DNR delivers consistent improvements across both backbones, thereby validating the generalizability of our denoising formulation for reranking architectures.
To further investigate different noise generators, we conduct ablation experiments on the heuristic noise generators that generate noise from the Gaussian distribution and Beta distribution. denoted as "w/ G" and "w/ B" respectively. The results are presented in Table 3 By comparing "w/ G" with "DNR-G or comparing "w/ B" with "DNR-B", we can see that our DNR Tramework-equipenr with model-based noise generator for adversarial coaming-consistnntty outperforms counterparts using heuristic noise generators.
RQ4:
RQ5: To compare different learning objectives in Eq. ( we first observe that "w/ G" and "w/B" methods outperform the PRM baseline, indicating the effectiveness of Lz, which uses the heuristic noise generator to augment the learning of reranker. Then, all "w/o Lasv" alternatives and "w/o L," alternatives appear to downgrade the effectiveness of the full implementation of DNR. All these observations validate the effectiveness of the derived terms in Eq.
8

---

Denoising Neural Reranker for Recommender Systems
Table Ablation Study for the different ways of leveraging the retriever scores on both PRM and Pier reranker backbones.
ML-IM
Kuaivideo
Book
Methods
1166
N@6
M@6
F1@6
AUC
11@6
N@6
M@6
F1@6
AUC
I1@6
N@6
M@6
F1@6
AUC
PRM
60.09
72.85 62.21 65.51 88.20 39.92 55.93 42.97 46.18
85.15
67.86 76.88 66.44 70.42 92.00
score
62.501
76.73 67.361 68.29 91.35t 46,201 67. 67.581 56.311 54.25t
85.761
69.711 77.99t 68.201 72.291 92.151
score
62.69
76.05t 66.481
68.36f
90.771
45.831 66.771 56.251
53.971
83,47
71.501
81.181
72.721 74.181 91.41
score
62.481
76.68t 67.341
68.191 87.20
46.821 66.941 54.771
55.00f
84.05
72.731
82.211 74.151
75.451 91.81
DNR
64.23
77.12 67.30 70.01
92.08
50.30
70.15
58.12
59.16
93.38
75.50
83.53 75.54
78.30
94.23
Pier
62.74
75.99 65.98 68.74
90.43
45.35
65.11 52.55
53.35
90.93
71.14
80.22 71.62
73.74 92.26
score
62,33
76.51 66.951
69.18
90.551
46.28
66.091
53.981
53.76f
90.56
71.88 81.021
73.151
74.321 91.89
score
62.58
76.18f 66.521
68.851
90.31
46.83
68.211 55.881
54.66f
91.82
71.27 80.701
72.791
74.151 92.281
w score
62,987
76.66t 67.16t 69.371
90.581
45.761
66.821
54.551
53.33 90.45
72.03t
81.561
74.741 75.321 92.331
DNR
63.41 77.28 68.02 70.22
91.97 48.89
69.35 57.76 56.02 92.38 73.68 82.61 74.82 76.88 93.78
Table 3: Ablation Study for DNR alternatives. "G w/o Ladv" and "B w/o Ladv" represent DNR-G or DNR-B paradigms without adversarial noise learning objective; "G w/o 2x" and "B w/o Lg" refer to DNR-G or DNR-B paradigms without the noise regularization.
ML-IM
Kuaivideo
Book
Methods
H@6
N@6 M@6
F1@6
AUC H@6 N@6 M@6 F1@6 AUC
H@6
N@6
M@6
F1
AUC
PRM
60.09
72.85
62.21
65.51
88.20
39.92
55.93
42.97
46.18
85.15
67.86
76.88
66.44
70.42
92.00
w/C
63.36
76.69
66.82
69.34
91.98
47.84
67.34
55.27
56.28
92.43
73.38
81.32
72.89
76.18
93.38
w/
63.65
76.90
67.01
69.64
90.85
48.83
68.71
56.60
57.45
92.73
73.42
81.20
72.74
76.23
93.46
G w/o Cade
63.71
76.57
66.61
69.43
92.14
48.75
68.70
56.57
57.34
92.21
73.83
81.89
73.27
76.60
93.62
w/o Lx
63.47
76.43
66.43
69.19
91.90
48.88
69.10
57.18
57.57
92.69
73.99
82.01
73.44
76.76
94.29
DNR-G
64.89
77.67
68.0
70.71
92.75
49.61
69.60
57.67
58.39
93.20
74.83
82.57
74.24
77.65
94.46
B w/o Calv
63.65
76.90
67.01
69.64
90.85
49.55
69.84
58.04
58.31
92.91
74.71
82.49
74.14
77.52
94.01
B w/o Lx
63.32
76.62
66.76
69.29
90.60
49.96
69.93
58.00
58.77
92.81
74.42
82,20
73.74
77.23
93.96
DNR-B
64.23
77.12 67.30 70.01
92.08
50.30
70.15
58.12
59.16
93.38
75.50
83.53
75.54
78.30
94.23
4.4 SENSITIVITY ANALYSIS (RQ6)
We conduct sensitivity analysis for Åco Ams and X for both DNR-G and DNR-B, and present the results in Figure 3 When adjusting Ams de is fixed at 40; when adjusting λe, Amis fixed at 0.3. For A adjustments, Am and Ag are better than Gaussian noise in most settings, as shown in Table there exist some common patterns in these hyper-paranee parae-paramtters:: 1) all parameters appears to have an optimal value within the searched region, indicating the effectiveness of including each corresponding designs, i.e., A. for the synthetic noisy scores in Eq.44). Am for the combined denoising loss Eq.(6), and X for the switching from heuristic noise generator to model-based one; 2) Over-amplifying these hyper-parameter results in the downgrade of the rerank performance, indicating the significance of keeping balance between the involved components. Additionally, the analysis of X in the left plot of Figure 3 shows that using the model-based noise generator can further enhance the performance over the heuristic one.
0.6 and 80, respectively. While the Beta noise generally performs
5 CONCLUSION
In this paper, we provide evidence that the initial retriever scores are informative for the latter reranker stage in the multi-stage recommender system. To formulate the relationship between retriever and reranker scores, we view the reranker as noise reduction task from the retriever scores and

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Denoising Neural Reranker for Recommender Systems
Sensitvity A.
Sensitvity An
Sensitvity
0.74
wy
DNR-G DNRWI
0.71
DAR-G
NOC586
0.70
NDCGgS
NDCG06
0.70
0.69
DMB-6
0.68
0.68
0.67
0.66
0.68
3.64
0.55
0.2
0.4
0.6
0.5
1.0
0.2
0.4
0.5
0.8
1.0
50
100
150
200
Figure 3: Sensitivity analysis of hyperparameters (i.e., Ac Ams and Xe) for our method on the
Kuaivideo dataset.
propose DNR. To fully explore the score space, we propose the framework of DNR, adding noise to generate retriever scores as augmentation and then denoising them align with user feedback. While a heuristic design of the noise distribution can effectively improve the model performance (e.g., Gaussian or Beta distribution), we prove that adversarially learns denoising reranker and noise generator for the retriever stage can further boost the reranker's performance. Theoretical analysis and empirical validation across three public datasets demonstrate the effectiveness of the noise reduction formulation and the design of our DNR.
ACKNOWLEDGEMENT
This research was supported by the National Natural Science Foundation of China (U25A20445) and the advanced computing resources provided by the Supercomputing Center of the University of Science and Technology of China (USTC). This work was also supported by Kuaishou Technology, where author Wenyu Mao conducted this research during an internship.
ETHICS STATEMENT.
This work is designed to explore the significance of retriever scores for the reranker, formulating the reranker denoising process of retriever scores with adversarial framework. We do not foresee any direct, immediate, or negative societal impacts of our research.
REPRODUCIBILITY STATEMENT.
All the results in this work are reproducible. We have discussed the optimal hyperparameters and the details devices and software environments in Section 4.1 and Appendix D.2 For reproducibility, we release the code at https: http://ththaa.ccoo////e////e////
11/DNR
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Denoising Neural Reranker for Recommender Systems
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13

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Denoising Neural Reranker for Recommender Systems
A NOTATIONS
symbol
description
u
user request information including profile features and interaction history random variables of retriever scores on the candidate item set random variables of user feedback the candidate item set observed retriever scores and user feedback in data the generated retriever scores from the learned heuristic posterior model
x
Xu. Zn
x
go
the probabilistic representation of the reranker model the probabilistic representation of the posterior the noise generator that implements the posterior the probabílistic representation of the non-optimizable retriever the posterior of a given reranker
P6
fo
Px
Pitz
Pak
the probabilistic representation of ground truth user feedback
direet
the standard user feedback alignment loss given the retriever scores the user feedback alignment loss given generated retriever scores from posterior the adversarial loss that encourages the posterior to generate irregular noises the regularization of posterior
La.
Lady
fxx
Xe
the hyperparameter that controls the magnitude of noise injection in Eq. the hyperparameter that balances Ldirect and L3 when learning the reranker the hyperparameter that determines which epoch switches the noise generator
Am
Xe
Table 4: List of Notations
B PROOFS
B. DERIVATION OF THE THREE OBJECTIVES OUR SOLUTION
In this section, illustrate the derivation of the Eq, 5 Similar to the variational Bayesian inference Kingma & Welling (2014), we start by analyzing the KL divergence between the real retriever scores and synthetic noisy scores:
Pxix (x z..)
DKL DKL (x._p((((xx))llxxxxxxx.))) = -Eps
log Po lz..)
p(x, z..)
=-Eps
log Pd (x (2.)
log p(g.)
(8)
log p(x, Zu)
EEps
+ log p(z)),
log Ps (x Z..)
Note that key difference between Eq.(8) and the conventional derivation in Kingma & Welling (2014) is that the variational posterior Φ is not a latent factor encoder that reduces noise and dimensions, but an explicit noise generator that mimics the behavior of retriever scores. In terms the denoising problem formulation, it is also similar to one-step diffusion, but with key difference in that the real retriever scores x,, are not sampled by a manually defined distribution. Instead, an existing retriever Px serves the prior distribution.
14

---

Denoising Neural Reranker for Recommender Systems
Then, rearranging the terms of Eq. ( we can derive the corresponding objectives that influence the data likelihood as follows:
log p (Zuu)
pxx, Zu))
= Ex-ps [log Poi (x(()
DRL (px(( (Pxxx (x/2,))
Ex-opl
log Palx [x] + DEL (po(x( l(pxxx)))) DKL
(9)
90 22 x)
Ex-pp
log 1o x)] Ex-rs log Psta (2 xx
DxL (x)
which is equivalent Eq. ( It generally follows the derivation of the evidence lower bound in variational auto-encoders, except that we further decompose the first reconstruction term into the augmented denoising loss L2 -xx-ps log qo (z.zz())) and the adversarial learning loss Ladv
Exmps log
g=(x_(x) Intuitively, when qe is closely aligned with the real user feedback distribution Pas (e.
Prix, the adversarial term Ladv will be close zero. Minimizing Ladv would encourage the noise generator Ja to find samples where qo and Pax behave differently.
B.2 THE ANALYTICAL LIMITATION OF STANDARD RERANKING MODELS
In this section, we formally analyze the Eq. (3. Recall that the direct optimization goal Cairect xx=x, - -xx,<p. [log qo (z., [x,)), where the observed x., only comes from the retriever prior p. without posterior modeling. Note that we additionally define Paix as the ground-truth user feedback probability and Patz the posterior distribution of retriever scores conditioned user feedback. Both Patx and are not related to the reranker and they are merely determined by the retriever prior and the user. Then, we can extract the user behavior alignment eгтor qo follows and derive its relation the goal of maxy log )(
go Jo(2,, (Znl (Znl|x))
-E EEx,, -p, [log (z(z//xx), - -Examp
log Pel (Zm xx,))
Ex, rpp, [log Patx (Zu [x
go (Zu, (Zu|Xx) Pelx (Z. xx,)
-Exoma log
+ DKL. (px (xxx (xxx)lPPree (Xu (xulkz)) log (zu)
= llogg(zzu) = Ex, p. [log go xx)] ) + Exirpe log Lo (ZzzllX2))
Patx (2u(
DRI. (px (xx (Ppaa (xul)))))
(10)
which corresponds to Eq.
As we have discussed in section 3.2 the direct optimization of the first term Ldirect might cause misalignment with the data likelihood maximization goal, since the observed data does not provide sufficient information about the retriever score distribution and the reranker is unaware of the noise patterns in the retriever score space. In contrast, proposed solution solves this limitation by collaboratively learning the noise generator f along with the reranker. The resulting objectives in (q.(5) intentionally find synthetic noisy score distribution P& that aligns with the real retriever score distribution and explores adversarial cases in which the reranker fails to accurately denoise into user feedback (details in Section 3.4)
C LEARNING ALGORITHM
We present the overall learning process of our method in Algorithm i
15

---

end for Denoising Neural Reranker for Recommender Systems
Algorithm I: Training procedures of DNR
Input: Hyperparameters Åe, Åes and Am initial reranker and noise generator & Output: Optimized denoising reranker and the noise generator fa-
for Epoch do
2: if' Ae then
3: for Each sample (u, Xu. Z,,) do
4:
Generate c <~ fmodel for given and z, and form synthetic noisy scores x,, with Eq (4
5: Optimize reranker 0 based on Lg;
6:
Optimize noise generator & based on Lady and Lxi
7:
end for else
8:
9:
for Each sample (u1 Xu, Zu)) do
10:
Sample ~ fheerissie for given u and zu and form synthetic noisy scores x', with Eq (4
11:
Optimize reranker based on Lg: end for
13: end if
D DETAILED SETTINGS FOR OFFLINE EXPERIMENTS
D.I DATASETS
The ML-1M dataset is a public benchmark in the field of recommender systems, comprising 1 million ratings from 6,040 users across more than 3,900 movies. The Kuaivideo dataset is sourced from Kuaishou, popular short-video app with over 300 million active users daily. The Amazon Books dataset comprehensive collection of product reviews specifically focused on books available on Amazon. For all datasets, initially process by eliminating users and items with fewer than 20 interactions, to avoid the cold-start issue. For the retriever stage, we split the data into train and test subsets at ratio of 8:2, where each sample is composed of interaction history and item for prediction. For the reranker stage, we sort the interactions chronological order, using the last six interactions the item list revealed to after reranking. We present the statistics of the processed datasets for the retriever and reranker stages in Table 5 and Table 6 respectively.
Table Dataset Statistics for Retriever Stage
Table 6: Dataset Statistics for Reranker Stage
Dataset
Users
# Items
Actions
Dataset
Users
# Trems
Sequences
ML-IM
6,020
3,043
995,154
ML-IM
6.022
3,043
161.646
Kuaivideo
89,310
10,395
3,270,132
Kuaividen
89,416
10,395
513,010
Amazon-Tore Toys
35,732
38, 121
1,960,674
Amazon- Toys
35,736
38,121
311,386
D.2 HYPERPARAMETERS SETTING
Here we present the optimal hyperparameters' settings in Table
D.3 BASELINES
We detail the compared baselines of our main experiments in the following, including traditional recommenders, list-refinement methods, generator-evaluator methods, and diffusion-based methods. Traditional Recommenders: predict the scores of candidate items and rank them accordingly.
SASRec Kang & McAuley (2018) proposes self-attention based sequential recommendation model (SASRec) that captures long-term semantics and short-term dynamics using an attention mechanism.
Caser Tang & Wang 2018) introduces a convolutional sequence embedding recommendation model that captures sequential patterns using convolutional filters applied to latent space embeddings.
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Denoising Neural Reranker for Recommender Systems
Table 7: Hyperparameter setting for DNR on different datasets
Datasets
ML-IM
Kuaivideo
Book
Ae
0.4
0.3
0.5
Am
0.6
Ae
weight_ _
0.001
0.001
dropout
0.3
0.3
0.5
batch size
2048
2048
2048
emb_dim
128
128
128
GRU4Rec Hidasi et (2016) apply recurrent neural networks (RNNs) bassion-based recom- mendations, addressing the challenge of short user sessions without long-term profiles.
MiDNN Zhuang et al. (2018) proposes global optimization framework for e-commerce search ranking that considers mutual influences between items, using extended features and sequence generation to optimize ranking and maximize overall utility.
dist-Refinement Methods: encode the item lists from the previous stage and refine the rankings based this information.
DLCM[Ai et al. 2018) leverages local ranking context from top-retrieved documents to refine initial ranking lists. The model uses recurrent neural network to capture document interactions and an attention-based loss function, significantly improving performance over traditional learning-to-rank methods.
SetRank[Pang al. 2020) introduces neural learning-to-rank model that learns permutation- invariant ranking function directly on document sets. Utilizing multi-head self-attention blocks, SetRank captures cross-document interactions and achieves robust performance across varying input sizes.
PRM [Pei et al. (2019) addresses personalized re-ranking in recommendation systems by incorporat- ing user-specifie preferences into the re-ranking process. It considers user behavior and candidate list, enhancing both relevance and personalization the final recommendations.
MIR Xi et al. (2022) proposes to capture complex interactions between user actions and candidate list features across multiple levels, improving the accuracy and relevance of recommendations through a hierarchical reranking approach.
PenneratorEvalusaie Methods: generate multiple ranked item lists and evaluate them to select the best one for users.
EGRerank Huzhang al. (023) proposes evaluator-generator framework for learning-to- rank (LTR) in e-commerce applications. The framework includes an evaluator that evaluates recommendations involving context, a generator that maximizes the evaluator score through reinforcement learning, and a discriminator that ensures the generalization of the evaluator.
Pier Shi et al. 2023) follows a two-stage architecture with Fine- Fine-grained Permutation Selection Module (FPSM) and an Omnidirectional Context-aware Prediction Module (OCPM). FPSM selects top-K candidate permutations based on user interest using SimHash, while OCPM evaluates these permutations with an omnidirectional attention mechanism.
NAR4Rec Ren et al. (2044) explores the use of -un-auttreeressivve generative models for re-ranking in recommendation systems, addressing challenges such as sparse training samples and dynamic candidates by introducing a matching model, unlikelihood training to distinguish feasible sequences, and contrastive decoding to capture item dependencies.
Diffusion-Based Methods: utilize diffusion models to generate items or lists for recommendation.
DiffuRec Li et al. 2024) incorporates a diffusion process to inject noise into target item embeddings and reverse process reconstruct the target item representation.
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Denoising Neural Reranker for Recommender Systems
candidate
history:
candidate
Interaction
candidate Scores
hislory
8.82
8.98
89
28
0.82
0.98
80
28
0.8
0.60
60
28
MLP
Transformer
MLP
Transformer
Transformer
weight
600rl emo
Jooncat User State
800n0 emo
user_ state
User star_
candidato homs
simlarity
condidato homs
similantyy
Boms
similerity
Figure 4: Different ways leverage retriever scores.
DCDR Lin et al. (2024) introduces new framework that leverages diffusion models for the reranking stage in recommendation systems. DCDR extends traditional diffusion models with discrete forward process and a conditional reverse process to generate high-quality item sequences.
D.4 IMPLEMENTATION DIVERSE STRATEGIES FOR LEVERAGING RETRIEVER SCORES
Here, we present the detailed implementation of different ways to leverage retriever scores in Figure D.4 The left one refers to "c score", which leverages concatenate operation to combine the score features with encoded user states from users' interaction history. The moderate one represents "+ score", which incorporates the score feature with user states by simple plus operation. The right one is the "w score", which adopts the initial candidate score as the weight of candidate items.
E COMPLEMENTARY EXPERIMENTAL ANALYSIS
E. EXPERIMENTS OF RETRIEVER STAGE
Detailed Experimental Settings for Retriever Stage
We implement the retriever stage with the MF method]Koree et al. (2009), which aims improve the click rate performance. The dimension of the item embedding is 128, and the learning rate is 0.001. We randomly select negative items to train the MF method, enhancing the retriever's performance despite the item sparsity.
Experimental results of Retriever Stage
Here, we present the detailed results of the retriever stage in Table g
Table 8: Performance of the retriever stage, which adopts the matrix factorization (MF) model to rate and select the top-50 items from full item set.
Datasets ML-IM Kuaivideo
Book
AUC
79.20%
89.62%
88.82%
E.2 VISUALIZATION OF LEARNED NOISES
To visualize the generated noise distribution, we compare the density curve of the generated noise from different noise generators, including Gaussian noise, Beta noise, and learning-based noise. As demonstrated in Figure 5 the distribution of learned noise is more aligned with the distribution of true noise between retriever scores and user feedback. This means that the resulting posterior with model-based noise sampler better reduces the divergence term òg in (5 and potentially improve the alignment between our proposed solution with the three objectives and the data likeliiidd maximization goal.
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Denoising Neural Reranker for Recommender Systems
Distribution Gaussian Noise
Distribution Beta Noise
Distribution of Learned Noise
Distribution of Noise
Denstyy Curce
2.5
Dendity Curve
1.50
Density Curve
Derstyy Curve
1.25
2.0
Density
1.00
Density
1.5
1.00
Denalty
2.0
0.75
0.50
0.5
0.25
431
11
1.0
-0.5
1.0
-0.5
0.5
0.5
1.0
-0.5
0.0 Vallue
Value
Value
Figure 5: The visualization of generated noise from different variants.
E.3 PERFORMANCE DIFFERENT SETTINGS OF BETA- DISTRIBUTION
The U-shaped Beta distribution in paper is motivated by the binary nature of the user feedback scores and the uncertain initial guess, and it is already empirically outperforming the Gaussian distri- bution in our experiments. Since the heuristically designed beta distribution is not guaranteed to be optimal - unlike the model-based noise generator, whose learned noise distribution is demonstrated be closer the real noise distribution.
To explore different Beta distributions, we conducted additional experiments with DNR that used beta distribution Beta(2, 5), i.e., DNR-BL, which more aligned with the true noise distribution as well as our model-based generator (as shown in Figure 5). before switching to the model-based generator in epoch Ae Empirically, the results are presented in Table
Table Experiments with the beta distribution replicate the learned distribution.
MI-IM
Kuaividen
Book
Methods
NDCG@6
6
NDCG@6
MAP@6
NDCG@6
MAP@
DNR-B
77.12
67.30
70.15
58.12
83.53
75.54
DNR-BL
77.43
67.49
71.10
59.61
83.68
75.79
We can observe that using this more "aligned" beta distribution (i.e., DNR-BL) before epoch Xe offers a slight improvement compared to using the U-shaped Beta (i.e., DNR-B) before Ag. This reinforces our main claim: adopting a model- noise generator to learn and regularize noise distribution, aligning it more closely with the ground-truth noise distribution.
E.4 DETAILED RESULTS OF SENSITIVITY ANALYSIS
Here, we present the detailed results of the sensitivity analysis for different datasets in Figure 6 Figure 7 and Figure
E.5 EXPERIMENTS ON LARGER RERANKER BACKBONES
DNR is backbone-agnostic framework for reranking tasks, utilizing and denoising the noisy scores from the previous retrieval stage through adversarial learning. Recently, LLMs have become powerful tool for reranking tasks |Mo et al. (2025)). However, text- LLM4Rerank is not comparable in our setting offline or online, due to the lack of rich textual information (such as detailed item descriptions) that M---ed rerankers fundamentally rely on. To evaluate the effectiveness of DNR in large models, scaled up PRM baseline by significantly increasing the number of its Transformer layers, serving as a 'larger model" for comparison. We then tested our DNR framework, using this scaled-up PRM as its reranker backbone. The results presented in the table 10
We can observe that scaling up the backbone may improve the reranking result, comparing the larger PRM with the original PRM, comparing the larger DNR with the original DNR. Additionally, original DNR outperforms the scaled-up "larger model" baseline (larger PRM); The DNR framework may also generalize well larger model.
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Denoising Neural Reranker for Recommender Systems
Table 10: Experiments on larger models (expand transformers of PRM 10 layers) with ML-IM dataset.
ML-IM
Methods
IR@@6
NDCG@6
MAP@6
FI@6
AUC
original PRM
60.09
72.85
62.21
65.51
88,20
original PRM+DNR
64.89
77.67
68.00
70.71
92.75
larger PRM
62.95
76.09
66.72
68.61
88,68
larger PRM-DNR
65.87
78.72
69.33
71.95
89.19
E.6 ONLINE A/B TEST
To better investigate the effectiveness of our DNR in online environment, conduct A/B test on an industrial platform with video feeds to 100+ millions of users daily.
Detailed Experimental Settings for Online Test The multi-stage pipeline of the online system consists of three stages: retrieval, ranking, and reranking. In the final reranking stage, the top-6 items for user exposure are selected from a candidate set of 50 items generated by the ranking stage. During the ranking process, scoring model computes multi-diimensionll features for each item. Among these, we utilize PCTR(predicted probability that the video will be watched for more than 3 seconds) as the primary retrieval score Xu, which corresponds to the "realshow" metric in the online system. Note that the final reranking stage considers the combination of the retrieval stage and the middle ranking stage
holistic previous stage, so the output PCTR scores from the ranking stage are considered as "retrieval score" this final stage. DNR is applied to generate new set of scores for 50 selected items and the top-6 items are chosen the final exposure.
Experiment Results For our online experiments, the total traffic is randomly divided, with 12.5% allocated to the DNR method and 12.5% to the baseline method. Each experiment is conducted for at least 7 days to ensure the reliability of the results. The DNR method demonstrates a significant improvement in realshow(, i.e., the cumulative number of videos watched by users), increasing it by 1.089%. These results indicate that the DNR method can estimate PCTR more accurately, which is strongly correlated with realshow. Additionally, the industrial recommendation environment involves various other metrics in addition to the realshow metric, including but not limited to app usage time, watch time, share-rate, like-rate, and comment-rate. We provide the corresponding results in Table IT We can see that, in our scenario, watch-time and share-rate are slightly negatively impacted, while like-rate and comment-rate are slightly positively impacted, indicating potential trade-off between metrics. Notably, while the watch time exhibited a slight decrease, we additionally observed that the long-term DAU slightly increased by 0.01 % 10.012%,+0.01122 reaching statistically significant improvement. This potentially suggests that the slight reduction in viewing duration likely attributable to users exploring broader range of clicked videos, while more significant long-term indicators remain positive. Beyond these, the impact on all other metrics not statistically significant, which validates the effectiveness of our method. It is worth noting that achieving performance gains across all online metrics inherently a multi-objective challenge- area that remains a key focus for future work. Additionally, we believe applying DNR to other xTR signals (e.g., watch time and share-rate) holds significant potential for further improvements.
which nearly
E.7 EXPERIMENTS ON DIVERSE RETRIEVERS
To better illustrate the generalizability of DNR across different retrievers, we implemented the dual-tower (DT) model as stronger retriever. The comparison results in the retrieval stage summarized in Table 12 and the comparison results in the reranking stage are summarized in Table that DNR outperforms DT+PRM, validating the generalizability of our method and the sustained advantage of the denoising framework.
13
We
Theoretically, there will always be empirical error that upper bounds the overall recommender system, which aims to minimize the "optimizable error", and there will be no room for the reranker to "denoise the scores" if the retriever itself achieve this bound. Yet, this theoretical bound is still
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Denoising Neural Reranker for Recommender Systems
ML-1MM
Kuaivideo
Book
6.76
0.830
wr ONR
wr DNR
ml
6.77
DAR
HDCG96
0.820
0.68
6.75
0.66
0.810
0.805
0.64
0.2
0.4
0.5
0.5
1.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.5
1.0
26
Az
Ac
Figure Sensitivity of DNR to the hyperparameter da on different datasets, which controls the degree of noise injection.
ML-1M
Kuaivideo
Book
6.775
0.71
DNR-G
DNRIG
6.770
0,70
MDCGge
NBCG@4
0.765
0.58
0.82
0.67
6.760
0.68
0.85
160
Figure 7: Sensitivity of DNR to the hyperparameter Ae on different datasets, which decides the epochs where the adversarial learning starts.
0.74
0.78
DNR-G
DNR-G BNIM
D4B-
0.835
NDCG
0.76
0.825
0.75
0.68
0.815
DMR-6
DMR6
0.68
0.2
0.4
0.5
1.0
0.2
0.5
0.5
1.0
0.2
0.6
0.6
1.0
Am
Figure 8: Sensitivity of DNR to the hyperparameter An on different datasets, which controls the magnitude of the augmentation.
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Denoising Neural Reranker for Recommender Systems
metric
performance boost
statistically significant (p < 0.05)
overall app-time
-0.01
no
realshow
+1.089%
yes
watch-time
-0.091%
n0
share-rate
-0.758%
no
like-rate
+0.609%
n0
comment-rate
+0.574%
n0
save-rate
-0.690%
n0
profile-click-rat
+0.129%
n0
Long term DAU
+0.01 %
yes
Table 11: Extended online A/B test results.
impractical for a single retriever in most industrial platforms, due to various reasons including (but limited to) the computational bottleneck, multi-task trade-off, and filtering on large-scale dynamic candidate pool. A more advanced retriever means a smaller remaining error that can be optimized, which indicates that the reranker will solve simpler denoising problem and simpler reranker design. In this case, a diminishing marginal gain only indicates diminishing computational requirement in the reranking stage. In general, the advantage of the denoising formulation is maintained regardless of the retriever employed.
Table 12:
The AUC performance of the different retrievers, including the matrix factorization (MF)
model and dual-tower model.
ML-IM Kuaivideo
Book
MF 79.20%
89.62%
88.82%
DT 79.86%
90.38%
90.0%
Table 13: The performance comparison of rerankers on different retrievers.
ML-IM
Kuaivideo
Book
Methods
NDCG@6
MAP@6
NDCG@6
MAP@6
NDCG@6
MAP@6
MF+PRM
62.21
$5.93
42.97
66.44
MF+PIER
75.99
65.98
65.11
$2.55
80.22
71.62
MF+DNR
77.67
68.00
70.15
58.12
83.53
75.54
DT+PRM
76.68
67.37
65.77
53.41
81.18
72.72
DT+PIER
76.36
66.35
68.34
56.26
81.55
72.79
DT+
77.96
68.78
70.96
58.87
84.42
77.11
E.8 EXPERIMENTS ON DNR's DIVERSITY
Since biases in the retriever's scores may not align with ground-truth user preferences, they will be "denoised" they constitute noise in the score signal. Unlike alternative methods that leverage retriever scores directly. DNR's primary advantage lies in denoising unreliable scores enabled by its noise-aware training paradigm, as evidenced by Table For example, a specific user may like certain video if it is popular. When the noise-aware DNR finds this noise and corrects it, the resulting prediction becomes more accurate, more personalized, and more diverse.
We conduct additional experiments on the diversity of the final recommendations by comparing the Coverage@) and coverage @6 6 metrics of the rerankers' outputs. As shown Table 14 the experimental results demonstrate that DNR's 's denoising capability enables it to mitigate incorrect biases retriever scores, thereby achieving greater diversity compared to baseline rerankers (e.g., PRM and PIER).
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Denoising Neural Reranker for Recommender Systems
Table 14: The diversity performance comparison of DNR and different rerankers.
ML-IM
Kuaividoo
Book
Methods
C@1
C@6
NC@1
C@6
c@! 1
C@6
PRM
0.5366
0.9080
0.7609
0.9648
0.4364
0.8584
Pier
0.5429
0,9073
0.7554
0.9571
0.4238
0.8663
DNR
0.5626
0,9109
0.7923
0.9702
0.4786
0.8721
E.9 COMPUTATIONAL ANALYSIS
Theoretically, our method does not change the computational cost during inference since we can adopt the same reranking model structure (e.g., PRM Pei et al. (2019)). ). In the training phase, we include additional noise generator that may require O(1) computational overhead, whose cost depends on the generator's 's structure. Empirically, the generator does not have to be sophisticated, and the computational overhead can be trivial compared the reranker's training.
To better demonstrate computational cost, we compare the per-epoch training time of DNR against several baseline rerankers, including PRM Pei al. 2019), Pier[Shi [Shi et al. (2023). DCDR Lin et al. (2024), and "w/ score" where retriever scores are directly used as input to the reranker. In 15 we present the time cost comparison for different reranker baselines in each training epoch. The variant "w/ score" denotes methods incorporating retriever scores as input features, regardless of the specific integration method (e.g., concatenation, plus, weighted combination). Our proposed DNR demonstrates competitive training efficiency, with time costs comparable to both PRM and w/ score baselines across all datasets (ML-IM, IM, Kuai Video, and Book). This validates the computational efficiency of our approach while maintaining competitive performance.
Table 15: Comparison of time costs.
Datasets
ML-IM
Kuaivideo
Book
PRM
01m01s
02m21s
01m42s
Pier
01m54s
04m11s
02m36s
DCDR
02m27s
04m58s
03m02s
w/ score
01m03s
02m49s
01m46s
DNR
01m06s
03m
01m51s
F LIMITATION AND DISCUSSION
F.1 LIMITATIONS
Computational Cost: Compared to a direct optimization framework with Eq. 2 that only learns 6, framework requires extra efforts train posterior model á (if using learnable posterior) with three additional loss terms to back-propagate. This might multiply the training cost for several folds. Fortunately, & involved in inference, so the inference cost stays the same.
F.2 FUTURE DIRECTIONS
When user can engage with an item with multiple behaviors (e.g., like and share a news story), DNR may adapt to this scenario by simply formulating dilti-dimeeiional response space for 2, and x,- Yet, different behavior signals may typically follow a heterogeneous distribution where uniform modeling technique might become sub-optimal. Additionally, the problem may become even more sophisticated when there exist multiple retrievers that estimate the same metric, especially in industrial settings. this case, one may have to come up with a more articulated integration method to clarify the connections between the user feedback and different retriever scores. Thus, we believe it is worth further investigating how the proposed framework may accommodate the multi-task solutions.
Multi-task Scenarios:
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Denoising Neural Reranker for Recommender Systems
Joint Optimisation of Multi-stages: Since our "retriever-aware reranker" is complementary piece to the "reranker-aware retriever" work, we believe optimizing the two components end-to-end jointly is very promising direction for future research. The potential lies in tackling the multi- stage challenges: effectively aligning the divergent training objectives, reliably nack-propagating non-uniform feedback signals across the stages, and allowing flexible model switch for any stage.
Multi-stage vs. End-to-End: Recent advances in end-to-end recommenders have witnessed significant advances, and evidence Deng al. (2025;; ; Kong al. (2025) has shown that end-to-end recommenders are potentially more effective since they no longer need to worry about the consistency challenge the multi-stage framework. Yet, there is still no evidence of general superiority comparing these two paradigms, and there is still no optimal solution for fully consistent multi-stage recommender.
G USE OF LLMS
Large language models (LLMs) were used only aid writing palish-including refining sentence phrasing. logical flow, and prose LLMs did not participate in core research tasks (e.g., experiment design, data processing, model training, result analysis, drafting key technical content).
altering original meanings or technical details.
24


Reviews:
[{"review_id": "qkxJefhIXe", "reviewer": "Reviewer_XnLA", "summary": "This paper proposes DNR (Denoising Neural Reranker), an adversarial framework for retriever-aware reranking in multi-stage recommender systems. Unlike prior work focusing on retriever optimization, DNR treats reranking as a noise reduction problem over retriever scores.\nIt jointly optimizes three objectives — denoising, adversarial noise generation, and score distribution regularization — to align with user feedback. Experiments on public datasets and an industrial system show consistent improvements over strong baselines.", "questions": "1. How sensitive is DNR to the quality of the underlying retriever? Would the advantage of denoising diminish when paired with a stronger retriever (e.g., ANN-based)?\n2. In the online experiment, some engagement metrics decrease slightly — could this reflect overfitting to short-term user feedback signals?\n3. Could the denoising concept be extended to a joint retriever–reranker optimization framework, where both components are trained end-to-end?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "• Novel perspective: The paper reformulates reranking as a noise reduction process over retriever scores — an underexplored yet conceptually important viewpoint.\n• Comprehensive framework: The proposed approach integrates denoising, adversarial noise generation, and distribution alignment within a unified learning objective, forming a coherent and well-motivated framework.\n• Strong empirical results: The method demonstrates consistent and notable improvements across three public benchmarks as well as in a large-scale industrial deployment, supporting its practical effectiveness.", "weaknesses": "• Limited retriever diversity: The retriever stage relies solely on a basic Matrix Factorization model. Stronger or more modern retrievers (e.g., dual-tower or ANN-based models) are not evaluated, which limits the generalizability of the conclusions.\n• Marginal online gains: The online A/B test reports only a +1.09% improvement in realshow, while other engagement metrics (e.g., share rate, watch time) show slight declines. This raises concerns about the robustness and stability of DNR in real-world settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "W1alOxcs52", "reviewer": "Reviewer_uV9W", "summary": "This paper presents a novel approach to the reranking stage in multi-stage recommender systems. The authors reframe the reranking task as a noise reduction problem, where the goal of the reranker is to \"denoise\" the scores provided by an earlier, less sophisticated retriever stage. To address this, they propose the Denoising Neural Reranker (DNR), an adversarial framework composed of a denoising reranker and a noise generation module. The framework is optimized through three objectives: an augmented denoising loss that aligns the reranker with user feedback using both observed and synthetic retriever scores; an adversarial loss that encourages the noise generator to produce challenging samples to improve reranker robustness; and a score distribution regularization term to ensure the synthetic scores resemble real retriever scores.", "questions": "Please refer to weaknesses part.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The conceptualization of reranking as a denoising process is an interesting and creative perspective on the interplay between stages in a cascaded recommendation pipeline. This approach moves beyond treating the stages as independent modules and instead explicitly models their relationship, which is a valuable direction for research in this area.   \n2. The authors have conducted a thorough set of offline experiments. The ablation studies, which systematically evaluate the contribution of each loss component and the choice of noise generator, provide clear insights into the model's mechanics.", "weaknesses": "1. The proposed DNR method is conditioned on the scores from an upstream retriever. This creates a significant risk of inheriting and amplifying any systemic biases (e.g., popularity bias) present in the retriever's output, which could harm the diversity and fairness of the final recommendations. Were any beyond-accuracy metrics evaluated in the offline experiments?\n2. The paper's motivation is to improve large-scale, multi-stage industrial recommender systems. However, a key dataset used for evaluation, ML-1M, is small enough that a complex retrieval-reranking pipeline may not be necessary. Could the authors comment on the justification for this architecture on this scale and discuss how they expect the findings to generalize to truly large-scale corpora (e.g., with millions of items).\n3. The pattern of increased short-term views (realshow) alongside decreased long-form engagement (watch-time) is a potential red flag. It may indicate that the model is promoting more \"clickbait\" or superficially appealing content that fails to retain user attention, which could be detrimental to long-term user satisfaction and retention. Presenting this outcome as a clear victory is a misleading interpretation of the complex dynamics of online recommender systems.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3ZyBj3BYQb", "reviewer": "Reviewer_Xkya", "summary": "This paper focuses on designing a retriever-aware reranker for multi-stage recommendation. Motivated by the empirical observation that the reranker may serve as a denoiser for the retriever, the authors propose an adversarial denoising framework DNR, which incorporates three components beyond the original reranking objective: (1) denoising loss, (2) adversarial noise generator loss, and (3) score distribution KL divergence term. Comprehensive experiments on three public datasets and one online A/B test on the industrial platform demonstrate the effectiveness of the proposed method.", "questions": "**Motivation.** This paper is motivated by the empirical observation that the reranker serves as a denoiser for the retriever. However, the explanation for this claim in Figure 1 is not very clear, as the T-SNE of the embeddings does not directly reflect the denoising effect. I suggest the authors show the distribution divergence between the retrieved/reranked scores and the ground-truth labels to directly support this claim.\n\n**Comments on Experiments.** Some clarifications on the experimental results are needed:\n- In Figure 3, the performance of DNR with different noise proportions $\\lambda_c$ stays the same, and why?\n- In Figures 3 and 6-7, the NDCG@20 performance is reported. Compared to NDCG@6 results in Table 2, is the results in these figures are actually NDCG@6? Moreover, the peak metrics of the three curves in Figure 3 are not the same, why?\n\n**Compatibility with Reranker-aware Retriever Methods.** There are various works on designing reranker-aware retrievers, as mentioned in Section 2.1. Can the proposed DNR be combined with these methods to further improve the overall performance?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The proposed objective is theoretically well-motivated, which serves as an upper bound of the negative log-likelihood of the data.\n- The experiments are comprehensive, including both offline comparisons with baselines, sufficient ablation studies, and an online A/B test, which validates the effectiveness of the proposed method.\n- The writing and derivation is clear and easy to follow.", "weaknesses": "In general, this paper is well-written and supports its claims with sufficient experiments. No major weaknesses are found, while some questions are raised below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "z1MoOTSqrB", "reviewer": "Reviewer_6Pyu", "summary": "This paper introduces a novel “recall-score denoising” perspective and designs an adversarial training framework, DNR, that enables a reranking model to exploit recall scores more effectively, leading to substantial improvements in recommendation quality. The effectiveness and universality of the proposed method are verified through both offline and online experiments. In addition, the experimental section formulates six research questions and sequentially validates the overall efficacy of the approach.", "questions": "1. The legend in Fig. 1(b) obscures the main pattern; arranging the four sub-figures in a 2×2 grid would alleviate the overlap.\n2. The font color in Fig. 4 is sub-optimal and markedly reduces legibility; a higher-contrast palette should be adopted.\n3. Several passages lack clarity and fluency. For example, “Recommender systems aim to personalize a list of items that maximizes user-engagement signals, such as purchases in e-commerce, video plays in video platforms, and link sharing in social networks. To achieve a feasible solution with large-scale candidate pools and user requests, industrial solutions … typically employ a multi-stage architecture comprising a retriever and a reranker.”—should be split into two or three shorter sentences to enhance readability.\n4. Equation (5) decomposes −log p(z_u) into L_z + L_adv + L_x + δ_x, where δ_x = −D_KL(p_ϕ‖p_{x|z}) ≤ 0 is assumed to be “small” and is indirectly minimized through other losses. The manuscript, however, offers neither a convergence guarantee nor numerical evidence to justify this assumption; the motivation for treating δ_x as negligible remains unexplained.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. Focusing on multi-stage recommender systems, the paper argues that prior work overwhelmingly concentrates on “reranking-aware retrievers” while overlooking “retriever-aware rerankers”. Although retriever scores are information-rich, they are simultaneously noisy; reranking can therefore be viewed as a denoising procedure. Naïvely exploiting these scores—e.g., simply treating them as additional features: suffers from theoretical limitations and yields poor alignment with actual user feedback. To this end, we propose DNR (Denoising Neural Reranker), which couples a denoising reranker qθ with a learnable noise-generation module fϕ.\n\n2. The user-feedback alignment objective is decomposed into three losses: (i) Lz, the denoising loss, trains the reranker to discriminate observed from synthetic scores; (ii) Ladv, the adversarial loss, encourages the generator to produce hard-to-denoise samples; and (iii) Lx, a KL-regularizer, aligns the synthetic score distribution with the empirical retriever distribution. \n\n3. Experiments on ML-1M, Kuaivideo and Amazon-Books compare DNR against both traditional recommenders (e.g., SASRec) and specialized reranking baselines (PRM, PIER). Six research questions (RQ1–RQ6) systematically demonstrate DNR’s superiority, including its generalization capacity and the impact of different noise generators. An industrial Online A/B test further confirms its practical utility.", "weaknesses": "1. Several figures are of sub-optimal quality and remain to be refined (e.g., Fig. 1(b) and Fig. 4); likewise, some textual descriptions and typesetting details, such as the presentation of RQ1–RQ6 in Section 4 EXPERIMENTS: call for improvement. Overly long sentences (e.g., the multi-stage architecture description in the introduction) impair readability.\n\n2. The proposed DNR framework is thoroughly compared with a variety of state-of-the-art reranking baselines (PIER, NAR4Rec, DCDR, etc.). The experimental protocol is comprehensive and the results are convincing. Nevertheless, a highly related recent work: “Comprehensive List Generation for Multi-Generator Reranking”(has neither been cited nor included in the comparison). Like DNR, it focuses on enhancing the reranking stage’s capacity to model candidate lists. Incorporating this baseline would offer a more complete assessment of DNR’s relative merits and would strengthen the paper’s timeliness within the rapidly evolving reranking literature.\n\n3. In the online A/B test, RealShow improves markedly, whereas the remaining metrics are either negative or neutral; however, no explanation is provided. RealShow denotes the cumulative number of users who watched videos, yet overall app-time and watch-time decrease. This pattern suggests that the optimized model may surface content that users are willing to click but do not actually prefer, implying that the deployed model’s performance could be inferior to its predecessor. A detailed analysis is warranted.\n\n4. When introducing the noise generator in Section 3.3, the authors borrow directly from diffusion models (Ho et al., 2020) and VAEs (Kingma & Welling, 2014), but they do not justify why the Beta distribution is a “natural” prior or why a model-based perturbation scheme should be preferred over heuristic alternatives: empirical verification alone is supplied.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)