Title: TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Venue: NeurIPS 2025
Accepted: yes
Keywords: Recommender Systems

Regression Models in Recommender Systems
Abstract: Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields have devised effective bias correction methods, all of them are post-hoc cures externally to the model, facing practical challenges when applied to real-world recommender systems. Hence, we propose a preemptive paradigm to eradicate the bias intrinsically from the models via minor model refinement. Specifically, a novel TranSUN method is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, termed Generalized TranSUN (GTS), which not only offers more theoretical insights but also serves as a generic framework for flexibly developing various bias-free models. Comprehensive experimental results demonstrate the superiority of our methods across data from various domains, which have been successfully deployed in two real-world industrial recommendation scenarios, i.e. product and short video recommendation scenarios in Guess What You Like business domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M), to serve the major online traffic.

TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems

Abstract

Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields have devised effective bias correction methods, all of them are post-hoc cures externally to the model, facing practical challenges when applied to real-world recommender systems. Hence, we propose a preemptive paradigm to eradicate the bias intrinsically from the models via minor model refinement. Specifically, a novel TranSUN method is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, termed G eneralized T ran S UN ( GTS ), which not only offers more theoretical insights but also serves as a generic framework for flexibly developing various bias-free models. Comprehensive experimental results demonstrate the superiority of our methods across data from various domains, which have been successfully deployed in two real-world industrial recommendation scenarios, i.e. product and short video recommendation scenarios in Guess What You Like business domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M), to serve the major online traffic.

1 Introduction

Regression models play a pivotal role in recommender systems, which have been widely applied to prediction tasks for targets including user engagement duration [38, 9, 23, 22], transaction amount [15, 42, 44], lifetime value [26, 41, 40, 21, 24], etc . Mean squared error (MSE) loss is a predominant choice for loss functions of regression models, which adopts for target y and features x an apriori assumption of y | x ∼ N ( f ( x ; θ ) , σ 2 ) , where N ( f ( x ; θ ) , σ 2 ) is a Gaussian distribution with f ( x ; θ ) as mean and σ 2 as variance and f ( x ; θ ) is a θ -parameterized predictor aiming to learn E y ∼P (Y | x ) [ y ] . However, the real target distribution frequently violates the Gaussian assumption in recommendation scenarios due to the high-skewed characteristics of the target data [7, 41, 40], which critically impedes the model convergence. This problem motivates prevalent adoption of various transformations T for target y in the MSE-based regression models, abbr. transformed MSE, which facilitates the conformity between the transformed distribution P ( T(Y) | x ) and the hypothetical distribution N ( f ( x ; θ ) , σ 2 ) , thereby improving the performance of model convergence [32, 31].

Though effective, it leads to the retransformation bias problem [2, 31, 37], i.e. the estimate for E y ∼P (Y | x ) [ y ] is biased when the prediction f ( x ; θ ) of the transformed MSE model is back-

transformed to the original space, as

̸

where T -1 is the inverse function of T . This critical issue has been conspicuously neglected within the community of recommender systems. Fortunately, there are still many effective works in other fields specifically studying the bias problem, all of which resort to the statistics of the model residuals to design post-processing bias correction mechanisms exactly after the model has been trained [12, 13, 33, 31, 14]. Despite the progress, all the prior works are posthoc approaches designed and applied externally to the model, which face practical challenges when applied to the real-world recommender systems (detailed in Appendix C). Therefore, we challenge the prior paradigm by asking: Can we eradicate the retransformation bias intrinsically from the model in a preemptive manner?

To answer this, this paper proposes a novel preemptive paradigm to eradicate bias from the model exactly during the training phase via minor model refinement. Specifically, we propose the novel TranSUN method to empower the trans formed MSE model with intrinsic un biasedness via introducing a jointly-learned auxiliary model branch for explicit bias modeling. Our method not only achieves theoretically guaranteed unbiasedness but also exhibits superior empirical convergence, as shown in Fig. 1. Interestingly, we point out that not all explicit bias modeling schemes bring theoretically guaranteed unbiasedness, and we further generalize TranSUN into a novel generic regression model family, termed G eneralized T ran S UN ( GTS ), to shed light on

0

250

500

750

1000

1250

1500

1750

2000

Training Steps (K)

0.0

0.5

1.0

1.5

2.0

2.5

Batched PGR

MSE PGR

LogMSE PGR

Our PGR

MSE Loss

LogMSE Loss

Our Loss

0.0

0.2

0.4

0.6

0.8

1.0

Normalized Loss

the underlying mechanism to intrinsically maintain model's unbiasedness. Essentially, GTS establishes the intrinsic unbiasedness through exploiting target transformation with conditional linearity based on particular point estimates [20] of T( y ) | x , exhibiting a more general model assumption compared to the vanilla MSE model. With this generic framework, not only various bias-free regression models can be directly devised to cope with varying data scenarios, but also any given regression model can be flexibly debiased in a plug-and-play manner even if the given model does not utilize any target transformation.

The major contributions can be summarized as follows.

We propose a novel preemptive paradigm to eradicate retransformation bias intrinsically from regression models to challenge the conventional ones, which is also the first work in the recommender system community to address the long-neglected bias problem.

We propose the novel TranSUN , a theoretically grounded, empirical convergence-friendly, and transformation-agnostic methodology, to endow the trans formed MSE model with intrinsic un biasedness exactly during the training phase.

We further propose a novel regression model family, termed G eneralized T ran S UN ( GTS ), to offer more theoretical insights, which also provides a generic framework for flexibly developing various bias-free regression models. Notably, our TranSUN and GTS have been validated to be directly applicable to other fields beyond the recommender system domain.

Extensive experiments on synthetic and real-world data validate the superiority and broad applicability of our methods. Now, TranSUN has been deployed in two core recommendation scenarios, i.e. product and short video recommendation, in Guess What You Like domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M) to serve the major online traffic.

2 Related works

Regression models in recommender systems mainly fall into two categories, i.e. value regression and classification-based regression, and our method belongs to the former. Value regression defines target as a continuous random variable and directly predicts target value using the expectation of the hypothetical distribution [22, 7, 48, 41, 42, 44], e.g. Gaussian distribution adopted by the MSE-based model family [42, 44, 43], ZILN distribution [40, 41]. By contrast, classification-based regression defines target as a discrete random variable via dividing it into multiple bins [45, 46, 9, 47], which mainly introduces either hierarchical routing [23, 21, 24, 39] or ordinal dependencies [23, 38] to boost models. Despite the progress, all the prior works neglect the study of model biasedness problems ( e.g. retransformation bias problem), which probably lead to the systematic errors in the model predictions (as shown in Eq. (3)), e.g. systematic underestimation of duration models in video recommender systems. Differently, our paper specifically addresses this critical biasedness problem.

Retransformation bias was initially identified in [2], with several works deriving theoretical correction terms by assuming the transformed distribution [13, 3, 8, 31]. Subsequent studies advanced correction frameworks free from distributional assumptions [12, 35, 5, 37, 27], achieving notable success across various fields [11, 19, 18, 30, 25, 1, 14, 34, 36, 33, 37]. Nonetheless, these approaches are still post-hoc remedies out of the model, bringing practical challenges when applied to industrial recommender systems. Differently, we preemptively address the bias via intrinsic model refinement.

Conditional transformation models (CTM) learns a parametric transformation introducing interaction between target y and input x to transform them into variables of assumed distributions [16, 28, 4]. CLTM is further restricted to using affine transformation for enhanced interpretability [29], whose model assumption is α ( x ) + h 0 (Y x ) · β ( x ) ∼ N (0 , 1) with Y x denoting y | x and h 0 ( · ) being parameter-free. Though CLTM and our GTS are superficially similar, they have one key differences, i.e. different dependencies in model assumptions, where the slope β ( x ) in CLTM's is only related to x , while ours (Eq. (11)) is related to both x and y . In addition, regarding model classes and motivations, CLTM belongs to transformation models [17] for interval estimation, while GTS is a value regression model for unbiased point estimation.

3 Method

3.1 Preliminary

Given a training set D = { ( x, y ) | ( x, y ) ∼ P (X , Y) } , where x denotes the input features and y denotes the continuous target label, the goal of the regression task is to make the model prediction f ( x ; θ ) and the ground truth y as close as possible, where θ is the parameter of the model f . In the recommender system community, a transformed MSE model is commonly adopted to boost the model convergence performance on high-skewed data, which is formulated as

where T is a customized bijective mapping. However, since minimizing Eq. (2) makes f ( x ; θ ) be an estimate of E y ∼P (Y | x ) [ T( y ) ] (derived in Appendix B.1), directly back-transforming f ( x ; θ ) leads to the retransformation bias problem, as shown in Eq. (1). Taking a common example in the industrial community, assuming the inverse transformation T -1 is convex, e.g. T( y ) is log( y +1) or √ y , then the retransformation bias will result in the systematic underestimation of model prediction ˆ y | x , as

where ≤ is obtained by Jensen's inequality and it is reversed when T -1 is concave.

3.2 TranSUN: joint bias learning

To intrinsically eradicate the retransformation bias, we propose TranSUN, which introduces an auxiliary branch z ( x ; θ z ) to the transformed MSE model to explicitly learn the bias. Specifically,

we employ a multiplicative bias modeling scheme, i.e. supervising z ( x ; θ z ) to learn the ratio of the ground truth y to the biased prediction T -1 ( f ( x ; θ ) ) , hence the bias learning loss is formulated as

where stop _ grad is applied to prevent the gradients of L T sun from passing to θ , | x | denotes the absolute value of x , and ϵ is a positive hyperparameter for avoiding division by zero. The reason for adopting this scheme is that the target ratio has intuitively small variance, empirically making the loss much smoother (as shown in Fig. 3a and discussed in Appendix A.2). During training, our model jointly learns the bias modeling task and the main regression task to achieve preemptive debiasing, thus the total loss is formulated as

In the testing phase, the model prediction ˆ y | x is formulated as

Theoretical analysis of unbiasedness. To illustrate the inherent unbiasedness of our method, we prove the prediction ˆ y | x to be an estimate of E y ∼P (Y | x ) [ y ] as follows (see Appendix B.2 for details). Owing to the stop _ grad operation, the optimization of the total loss L T TranSUN can be decomposed into two independent parts, as

In the second part, the prediction ˆ y | x is derived as the estimate of E y ∼P (Y | x ) [ y ] via

Key discussion. According to Eq. (8), we propose an interesting argument: not all bias learning schemes provide theoretically guaranteed unbiasedness . As an example, supposing we turn the learning target of z ( x ; θ z ) in Eq. (4) to the ratio of biased prediction to ground truth, i.e. T -1 ( f ( x ; θ )) /y , then the model prediction is derived as an estimate of 1 / E y ∼P (Y | x ) [ 1 y ] (derived in Appendix B.3), which is a biased estimate bringing model underestimation according to Eq. (3). More empirical results are shown in Table 5. Therefore, in the next section, we point out that it is conditional linearity as the essential mechanism for the unbiasedness of TranSUN, rather than the explicit bias learning.

3.3 Generalized TranSUN (GTS)

In this section, we generalize TranSUN to a novel generic regression model family, termed Generalized TranSUN (GTS), to provide more insights into the intrinsic unbiasedness mechanism of our method. The total loss function of GTS is formulated as

where H q is a known apriori distribution parameterized by x and θ q , f ( x ; θ q ) is the expectation of the hypothesized distribution H q ( x, θ q ) , and κ is a parameter-free function with κ ( · ) > 0 .

Specifically, the first loss term L H q is termed conditional point loss , which supervises f ( x ; θ q ) to specifically learn a conditional point estimate Q y ∼P (Y | x ) [ T( y ) ] ( e.g. arithmetic mean, median, mode) via customizing the apriori conditional probability assumption of T( y ) | x ∼ H q ( x, θ q ) , with

The second loss term in Eq. (9) is termed linear transformation loss , which much resembles lineartransformed MSE, whereas the key difference is that our slope κ ( f ( x ; θ q ) ) is dynamically generated by the learned conditional point f ( x ; θ q ) . Additionally, due to the stop _ grad operation, the slope κ ( f ( x ; θ q ) ) is actually completely determined by L H q . In the testing phase, the prediction of GTS is ˆ y | x = z ( x ; θ z ) /κ ( f ( x ; θ q ) ) , which can also be proved unbiased by following the derivation process in Eq. (7, 8) (also derived in Appendix B.4).

0

10

(a)

RS-G

0.0

0.1

0.2

0.3

Density

mean = 2.00

variance = 2.00

skewness = 1.41

Right-Skewed: Gamma

0

50

100

(b)

RS-BU

0.00

0.02

0.04

0.06

0.08

Density

mean = 14.90

variance = 721.09

skewness = 2.62

Right-Skewed: Bi-Uniform

0

10

(c)

RS-ZIG

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Density

mean = 0.40

variance = 1.03

skewness = 3.29

Right-Skewed: Zero-Inflated Gamma

0.0

0.5

1.0

(d)

LS-B

0.0

0.5

1.0

1.5

Density

mean = 0.67

variance = 0.04

skewness = -0.51

Left-Skewed: Beta

0

50

100

(e)

LS-BU

0.00

0.02

0.04

0.06

0.08

Density

mean = 86.10

variance = 721.30

skewness = -2.62

Left-Skewed: Bi-Uniform

0

50

100

(f)

SM-U

0.000

0.002

0.004

0.006

0.008

0.010

Density

mean = 50.00

variance = 833.33

skewness = 0.00

Symmetric: Uniform

0

50

100

(g)

SM-TN

0.00

0.01

0.02

0.03

0.04

Density

mean = 50.00

variance = 833.33

skewness = 0.00

Symmetric: Trunc-Normal

0

50

100

(h)

SM-BU

0.00

0.01

0.02

0.03

0.04

0.05

Density

mean = 50.50

variance = 1988.39

skewness = -0.00

Symmetric: Bi-Uniform

Model assumption. Given any sample x , GTS essentially exploits a customized x -conditioned point ( abbr. Q ) to generate a Q -conditioned slope κ for linear transformation of target y . Hence, the model assumption of GTS is formulated as

where Y x = ( y | x ) and N denotes Gaussian distribution. As shown in Eq. (11), the slope κ is conditioned on P (Y | x ) , making GTS akin to a piecewise-linear-transformed MSE where the intervals are determined by the condition x . Accordingly, GTS can be viewed as a conditional lineartransformed MSE, the unbiasedness of which is naturally maintained since linear-transformed MSE is unbiased itself. Notably the violation of model assumptions will not invalidate the model's theoretical unbiasedness, which only affects the model performance ( e.g. convergence). Furthermore, according to Eq. (11), the variance of the hypothetical distribution of y | x is modeled as σ 2 /κ ( Q ) , where one can explicitly model the heteroscedasticity by specifying Q and κ with the domain knowledge, exhibiting a more general model assumption than the traditional MSE model.

Application. There are two main application scenarios for the proposed GTS. The first is to directly devise various bias-free regression models by customizing the assumption T( y ) | x ∼ H q ( x, θ q ) and κ 's function type to cope with different data scenarios. Some design guidelines are shown in Appendix A.6, with reference results provided in Table 15, 10. The second is to serve as a plugand-play debiasing module to directly eradicate bias from any given regression model, even if the given model does not use any transformation T . Specifically, for any given regression model M , one can easily establish the debiased model M ′ by setting T to identity transformation, L H q to the loss function of M , and f ( x ; θ q ) to the prediction of M . More empirical results are shown in Table 6.

4 Experiments on synthetic data

In this section, we conduct experiments on synthetic data to evaluate model performance. Specifically, we sample abundant instances { y i } N i =1 from the pre-defined distribution H (Y) to form the conditional distribution P s (Y | x ) , and then models are trained on { y i } N i =1 for learning regression task, after which the error between model prediction and the theoretical-calculated expectation E y ∼H (Y) [ y ] are computed for evaluation.

4.1 Experiment settings

Data and metric. We adopt 8 distributions H (Y) from three categories: right-skewed, left-skewed, and symmetric. Details are shown in Appendix D.1.1. Fig. 2 illustrates the density and some statistics of the sampled P s (Y | x ) . We adopt SRE (Signed Relative Error) as the metric, i.e. (ˆ y -y ) /y where ˆ y and y are prediction and ground truth, respectively.

Implementation details. See Appendix D.1.2 for the detailed introduction.

4.2 Results on synthetic data

Effectiveness on eradicating retransformation bias. Three transformations, i.e. linear, square, and logarithmic transformations, are employed to compare the performance between transformed MSE and our TranSUN. As demonstrated in Table 1, the retransformation bias problem arises in transformed MSE when the transformation is not linear, i.e. model becoming underestimated with T( y ) = ln( y +1) and overestimated with T( y ) = y 2 , which is consistent with Eq. (3). In contrast, TranSUN maintains unbiasedness ( | SRE | < 0.7% ) regardless of the transformation applied. Notably

MSE and linear-transformed MSE both exhibit unbiasedness in Table 1, that is because they converge well due to the extreme simplicity of input features and model architectures. Differently, MSE exhibits significantly poor convergence on real-world data, as detailed in the next section (Table 7, 8).

Comparison with state-of-the-arts. Our TranSUN is further compared with state-of-the-art regression models in the recommender system field. As shown in Table 2, our TranSUN achieves the best performance as it is the only method that maintains unbiasedness across all distributions. Subsequently, we provide an analysis of the biasedness of the baselines. The MAE model overestimates the mean on left-skewed and underestimates it on right-skewed data due to its median modeling approach. WLR exhibits an error of 0 . 2483 on RS-ZIG, roughly equivalent to p + / (1 -p + ) = 0 . 25 ( p + is the positive rate of RS-ZIG), consistent with the derivations in [9]. ZILN and OptDist both show significant biasedness on certain distributions, owing to these severely violating the LogNormal or ZILN distribution assumptions. MDME overestimates the mean on left-skew and underestimates it on right-skew data, likely because it estimates the mean of mode buckets under mode sub-distribution, which also explains its biasedness on SM-BU and SM-U as sampling error may result in severe mode estimation error on these data. TPM underestimates the mean on left-skew and overestimates it on right-skew data, likely because it learns the average of quantiles. Note that increasing bin number can reduce its bias, as shown in Sec E.1. CREAD presents a similar bias problem to TPM, whereas slightly alleviated due to its EAD binning method [38].

Comparison of GTS instance models. Some common L H q and κ are selected to devise different instances of GTS, providing reference results for its application. As exhibited in Table 15, in terms of L H q , MSE loss is empirically the most versatile loss as it fastly converges to the unbiased solution in all distributions, whose Q corresponds to the arithmetic mean point. Among other losses, MAE loss presents better convergence on right-skewed data, while MSPE and MAPE loss both converge more effectively on left-skewed and symmetric data. Regarding function κ , all candidates exhibit unbiasedness consistent with TranSUN. The performance of κ on real-world data is shown in Table 10.

5 Experiments on real-world data

5.1 Experiment settings

Datasets and metrics. We adopt two public datasets ( CIKM16 1 , DTMart 2 ) from different fields and one exclusive industrial dataset ( abbr. Indus ) collected from an e-commerce platform. Details are shown in Appendix D.2.1. Indus aims to predict the Gross Merchandise Value (GMV) of each order paid on the platform, where the Pareto phenomenon occurs, i.e. 80% of the GMV being contributed by the top 30% high-priced samples. Hence, we further conduct evaluation solely on the top high-priced samples, abbr. Indus Top test. Two metrics akin to SRE in Sec. 4.1 are specifically proposed for assessing unbiasedness (see Appendix D.2.2 for detailed discussion), i.e. Total Ratio Error (TRE) and Mean Ratio Error (MRE). Specifically, TRE and MRE are defined as | ∑ M i =1 ( ˆ y i -y i ) / ∑ M i =1 ˆ y i | and | 1 /M × ∑ M i =1 ( ˆ y i -y i ) / ˆ y i | , respectively, where ˆ y i is prediction, y i is ground truth, and M is sample number. We further adopt some metrics commonly used in the literature [43, 21, 23, 38], i.e. NRMSE, NMAE, XAUC, and NDCG (NDCG results are shown in Table 11).

Implementation details. See Appendix D.2.3 for the detailed introduction. TensorFlow-style implementation of our TranSUN and GTS is exhibited in Code 1. Some optional techniques for optimizing TranSUN's online performance are introduced in Appendix A.4. Moreover, the efficiency analysis of our method is demonstrated in Appendix A.3.

5.2 Results

Effectiveness on eradicating retransformation bias. Since these target data are right-skewed, commonly-used concave functions, i.e. logarithm, square, and arctan transformation, are adopted for performance comparison between transformed MSE and our TranSUN. Because retransformation bias can be viewed as fitting deviation, evaluations are conducted on the training sets of CIKM16 and DTMart. As shown in Table 3, transformed MSE exhibits significant bias issues, with arctan transformation being the most severe. Differently, TranSUN significantly mitigates the bias (mostly < -70.0% ) for all transformations. Furthermore, we assessed their performance on different target bins on Indus, where bins are obtained by dividing ascending targets into equal frequencies. In Fig. 3c, LogMSE and LogSUN denotes transformed MSE and TranSUN with T( y ) = ln( y +1) , respectively, with Signed TRE used as metric. As illustrated, LogSUN substantially alleviated bias across all bins compared to LogMSE. Moreover, LogMSE's bias issue gets more severe when the target value grows larger, and meanwhile, our LogSUN's debiasing intensity gets higher as well. Fig. 3c further shows κ gradually decreases as target y increases, to provide relatively stable κ · y for smoothing L ln sun .

Comparison with post-hoc correction methods. To further demonstrate the debiasing capability of our method, our LogSUN is compared against two classic bias correction methods, i.e. Normal Theory Estimates (NTE) [11] and Smearing [12], alongside a mainstream calibration method, i.e. SIR [10], on CIKM16 test. Different from LogSUN, all the baselines are post-hoc methods, which naturally confer them an advantage in the comparison. Nevertheless, as shown in Table 4, our method achieves comparable performance with these baselines. Among them, the poor performance of NTE

Models

Correction methods

TRE

↓

MRE

↓

NRMSE

↓

NMAE

↓

LogMSE

-

0.3451

0.3667

0.6189

0.4528

LogMSE

NTE [11]

0.1262

0.1442

0.5718

0.4369

LogMSE

Smearing [12]

0.0175

0.0477

0.5617

0.4335

LogMSE

SIR [10]

0.0198

0.0236

0.5821

0.4309

LogSUN

-

0.0123

0.0439

0.5625

0.4333

Scheme ID

L

T

TRE

↓

MRE

↓

NRMSE

↓

NMAE

↓

XAUC

↑

S0

(

z

(

x

;

θ

z

)

-

stop

_

grad

[

y

-

exp(

f

(

x

;

θ

))

])

2

0.0689

0.0829

0.6153

0.4459

0.6711

S1

(

z

(

x

;

θ

z

)

-

stop

_

grad

[

exp(

f

(

x

;

θ

))

/y

])

2

0.1288

0.1036

0.6255

0.4387

0.6730

S2 (Ours)

(

z

(

x

;

θ

z

)

-

stop

_

grad

[

y

·

exp(

-

f

(

x

;

θ

))

])

2

0.0123

0.0439

0.5625

0.4333

0.6740

is attributed to the transformed target not adhering to the Gaussian distribution assumptions. In contrast, Smearing and SIR exhibit superior results, as they are free from distributional assumptions.

Performance comparison with state-of-the-arts. Our TranSUN is compared against common regression loss ( e.g. MSE, MAE), transformed models ( e.g. LogMSE, SqrtMSE), advanced classification-based regression models ( e.g. TPM, MDME), and fancy value regression models ( e.g. ZILN, OptDist) on CIKM16 test and DTMart test. In Table 7, LogSUN and SqrtSUN denote our method with T( y ) set to ln( y +1) and √ y , respectively. As illustrated, our method achieves the best performance across our TRE and MRE metrics, as well as the commonly used NRMSE and NMAE metrics. Note that applying TranSUN usually results in a slight decline in XAUC. However,

this trade-off remains favorable as discussed in Appendix A.1. Furthermore, as shown in Table 8, consistent superiority of our LogSUN is observed on Indus test, where its XAUC for top high-priced samples also achieves the best performance ( > +0.015 uplift to LogMSE).

Extension to state-of-the-arts. To validate the effectiveness of GTS as a debiasing framework, we apply our method to several state-of-the-art models with significant bias issues on CIKM16 test, where κ ( f ( x ; θ q )) = 1 / ( f ( x ; θ q ) + 1) is adopted in Eq. (9). As depicted in Table 6, the bias issues of classification-based models ( i.e. MDME, WLR) and value regression models ( i.e. ZILN, OptDist) are all significantly mitigated ( < -70.0% in TRE and MRE) by integrating our approach.

Online A/B test. In Taobao App (DAU > 300M), our LogSUN is deployed to serve as a pointwise ranking model for GMV target in two recommendation scenarios ( i.e. product and short video recommendation in Guess What You Like business domain in Taobao App homepage), respectively, to optimize object distribution (see Appendix E.2 for details), which is compared against the previous online base (LogMAE) and LogMSE. Two common metrics, GMV and GMVPI (GMV Per Impression), are adopted to assess online performance. Due to the large variability of these GMV-related metrics, we allocated either substantial traffic ( e.g. 15% of the total traffic for LogSUN) or a sufficiently long period ( e.g. one month) for A/B tests to obtain statistically significant results. As shown in Table 9, LogSUN achieves significant gains ( e.g. > +1.0% GMVgains) over all the baselines. Now, LogSUN has been deployed to serve the major traffic.

5.3 Ablation studies

Comparison of different GTS instance models. To offer reference results for GTS's application on realworld data, some common L H q and κ are selected to build different instances of GTS for comparison. As shown in Table 10, MSE loss is the best-performing L H q , while MAE loss also performs well as the target data are right-skewed, which is consistent with the results in Table 15. Regarding function κ , compared to the one used in TranSUN, other κ candidates all bring about poorer loss convergence, which is probably because the variances of their κ · y are all too large (shown in Table 17), leading to an excessively steep optimization landscape for θ z .

Comparison of different bias modeling schemes. To show the superiority of the multiplicative bias modeling scheme in Eq. (4), two other intuitive schemes are compared with ours on the LogSUN model, as presented in Table 5, where notably neither of the scheme candidates (S0, S1) belongs to GTS family. The impact of different schemes on model training is illustrated in Fig. 3a, 3b. As visible, despite theoretically unbiased, S0 scheme exhibits poor convergence performance, likely due to

100

200

300

Training Steps

1.0

1.5

2.0

2.5

S0 Loss Value

1e7

S0 Loss (Left)

0

5000

10000

15000

20000

S1/S2 Loss Value

(a) Loss curves

100

200

300

400

Training Steps

0.0

0.1

0.2

0.3

TRE Value

S0 TRE

S0 MRE

S1 TRE

S2 TRE

0.05

0.10

0.15

0.20

0.25

0.30

MRE Value

(b) Metric curves

0

1

2

3

4

5

6

7

8

9

10

Equal-Frequency Target Bins

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Signed TRE

LogMSE

LogSUN

κ

0.00

0.05

0.10

0.15

0.20

0.25

Conditional slope:

κ

Value

(c) Binned STRE &

κ

-4

-3

-2

-1

0

1

2

log10 (

ε

)

0.00

0.05

0.10

0.15

Total Ratio Error (TRE)

TRE

MRE

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Mean Ratio Error (MRE)

(d) Impact of

ϵ

the high loss variance caused by its additive modeling scheme. S1 scheme converges well but suffers from significant bias, as it is not theoretically unbiased, as discussed in Sec. 3.2. In contrast, our scheme (S2) achieves both superior convergence and theoretical unbiasedness.

Architectural sensitivity. Since our method introduces an extra bias learning branch to transformed MSE, we explore the impact of different parameter sharing schemes between the added branch and the original model, which is conducted on the LogSUN model. In Table 16, sharing parameters from bottom layers ( e.g. embedding layers) has minimal impact on performance, whereas further sharing more parameters within MLP results in a noticeable performance degradation, which might be inferred that the added task primarily affects the downstream MLP-modeled fitting function rather than the distribution of latent embedding.

Impact of hyperparameters. We study the impact of the only hyperparameter ϵ on the LogSUN model using CIKM16. As visible in Fig. 3d, LogSUN stably demonstrates superior performance when ϵ is between 0 . 01 and 1 . 0 . As ϵ further approaches zero, the performance rapidly degrades. This may be because T -1 ( f ( x ; θ )) tends to be near zero at the early stages of training, thus y/ ( | T -1 ( f ( x ; θ )) | + ϵ ) gets extremely large when ϵ is close to zero as well, causing significant loss oscillation and hindering model convergence. Conversely, performance also degrades when ϵ ≥ 100 , likely due to y/ ( | T -1 ( f ( x ; θ )) | + ϵ ) being too small to be learned within the fitting scope of z ( x ; θ z ) , thus leading to the model underfitting.

6 Conclusion

In this paper, we propose a novel preemptive paradigm to eradicate retransformation bias intrinsically from the regression models to challenge the conventional ones, as the first work in the recommender system field to address the long-neglected bias problem. Specifically, a novel method, TranSUN, is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, Generalized TranSUN (GTS), which not only offers theoretical insights into TranSUN but also serves as a generic framework for flexibly developing various bias-free models. Extensive experiments demonstrate the superiority and broad applicability of our methods.

Limitations and future works. Regarding real-world applications, although the effectiveness of TranSUN has been validated across data from various domains, these target distributions are all right-skewed. Hence, we expect more exploration for its application where the target distributions are not right-skewed. Moreover, a limitation of our method is the decline in XAUC, which conforms to the No Free Lunch (as discussed in Appendix A.1), thus integrating isotonicity into TranSUN is a challenging but interesting direction. In the future, we plan to investigate GTS further, e.g. exploring a general methodology to identify the optimal point Q and function κ on any given data.

Acknowledgments

We deeply appreciate Han Wu, Chi Li, Kun Yuan, Runfeng Zhang, Weiqiu Wang, Longbin Li, Xin Yao, Nan Hu, Chuan Wang, and Hanqi Jin for their insightful suggestions and discussions. We thank Kewei Zhu, Yuxin Duan, Hao Fang, and Gaofeng Li for necessary supports about online serving. We thank anonymous reviewers for their constructive feedback and helpful comments.

References

Johan G Andréasson and Pavel V Shevchenko. A bias-corrected least-squares monte carlo for solving multi-period utility models. European Actuarial Journal , 12(1):349-379, 2022. 3

Maurice S Bartlett. The square root transformation in analysis of variance. Supplement to the Journal of the Royal Statistical Society , 3(1):68-78, 1936. 1, 3

Maurice S Bartlett. The use of transformations. Biometrics , 3(1):39-52, 1947. 3

Philipp FM Baumann, Torsten Hothorn, and David Rügamer. Deep conditional transformation models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pages 3-18. Springer, 2021. 3

Kenneth Belitz and PE Stackelberg. Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models. Environmental Modelling & Software , 139:105006, 2021. 3

Léon Bottou, Frank E Curtis, and Jorge Nocedal. Optimization methods for large-scale machine learning. SIAM review , 60(2):223-311, 2018. 21

Hsin-Yu Chen, Cheng-Te Li, and Ting-Yu Chen. Skewness-aware boosting regression trees for customer contribution prediction in financial precision marketing. In Tat-Seng Chua, Chong-Wah Ngo, Roy Ka-Wei Lee, Ravi Kumar, and Hady W. Lauw, editors, Companion Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, Singapore, May 13-17, 2024 , pages 461-470. ACM, 2024. 1, 3

Timothy A Cohn, Lewis L Delong, Edward J Gilroy, Robert M Hirsch, and Deborah K Wells. Estimating constituent loads. Water resources research , 25(5):937-942, 1989. 3

Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. In Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells, editors, Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , pages 191-198. ACM, 2016. 1, 3, 6, 8, 22, 27, 29

Chao Deng, Hao Wang, Qing Tan, Jian Xu, and Kun Gai. Calibrating user response predictions in online advertising. In Yuxiao Dong, Dunja Mladenic, and Craig Saunders, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part IV , volume 12460 of Lecture Notes in Computer Science , pages 208-223. Springer, 2020. 7, 8, 26

SPRUGEL DG. Correcting for bias in log-transformed allometric equations. Ecology , 64:209-210, 1983. 3, 7, 8

Naihua Duan. Smearing estimate: a nonparametric retransformation method. Journal of the American Statistical Association , 78(383):605-610, 1983. 2, 3, 7, 8

DJ Finney. On the distribution of a variate whose logarithm is normally distributed. Supplement to the Journal of the Royal Statistical Society , 7(2):155-161, 1941. 2, 3

Maurizio Galetto, Elisa Verna, and Gianfranco Genta. Accurate estimation of prediction models for operator-induced defects in assembly manufacturing processes. Quality Engineering , 32(4):595-613, 2020. 2, 3

Chang Gong, Di Yao, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su, and Jingping Bi. Causalmmm: Learning causal structure for marketing mix modeling. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining , pages 238-246, 2024. 1

Torsten Hothorn, Thomas Kneib, and Peter Bühlmann. Conditional transformation models. Journal of the Royal Statistical Society Series B: Statistical Methodology , 76(1):3-27, 2014. 3, 23

Torsten Hothorn, Lisa Möst, and Peter Bühlmann. Most likely transformations. Scandinavian Journal of Statistics , 45(1):110-134, 2018. 3

Jason Hunter, Mark Thyer, David McInerney, and Dmitri Kavetski. Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology , 603:126578, 2021. 3

Roy W Koch and Gary M Smillie. Bias in hydrologic prediction using log-transformed regression models 1. JAWRA Journal of the American Water Resources Association , 22(5):717-723, 1986. 3

Erich L Lehmann and George Casella. Theory of point estimation . Springer Science & Business Media, 2006. 2

Kunpeng Li, Guangcui Shao, Naijun Yang, Xiao Fang, and Yang Song. Billion-user customer lifetime value prediction: An industrial-scale solution from kuaishou. In Mohammad Al Hasan and Li Xiong, editors, Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 , pages 3243-3251. ACM, 2022. 1, 3, 6, 7, 8, 22, 27, 29

Chengzhi Lin, Shuchang Liu, Chuyuan Wang, and Yongqi Liu. Conditional quantile estimation for uncertain watch time in short-video recommendation. CoRR , abs/2407.12223, 2024. 1, 3

Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, and Peng Jiang. Tree based progressive regression model for watch-time prediction in short-video recommendation. In Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye, editors, Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , pages 4497-4506. ACM, 2023. 1, 3, 6, 7, 8, 22, 27, 29, 30

Wenshuang Liu, Guoqiang Xu, Bada Ye, Xinji Luo, Yancheng He, and Cunxiang Yin. MDAN: multidistribution adaptive networks for LTV prediction. In De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, and Jerry Chun-Wei Lin, editors, Advances in Knowledge Discovery and Data Mining -

28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III , volume 14647 of Lecture Notes in Computer Science , pages 409-420. Springer, 2024. 1, 3

X Liu, MC Freed, and PK McCutchan. Correction of retransformation bias in nonlinear predictions on longitudinal data with generalized linear mixed models. Journal of Biometrics & Biostatistics , 6(2):1, 2015. 3

Edward C Malthouse and Robert C Blattberg. Can we predict customer lifetime value? Journal of interactive marketing , 19(1):2-16, 2005. 1

Sushant More. Identifying and overcoming transformation bias in forecasting models. CoRR , abs/2208.12264, 2022. 3

Lisa Möst and Torsten Hothorn. Conditional transformation models for survivor function estimation. The international journal of biostatistics , 11(1):23-50, 2015. 3

Lisa Möst, Matthias Schmid, Florian Faschingbauer, and Torsten Hothorn. Predicting birth weight with conditionally linear transformation models. Statistical methods in medical research , 25(6):2781-2810, 2016. 3, 23

Michael C Newman. Regression analysis of log-transformed data: Statistical bias and its correction. Environmental Toxicology and Chemistry: An International Journal , 12(6):1129-1133, 1993. 3

Jerzy Neyman and Elizabeth L Scott. Correction for bias introduced by a transformation of variables. The Annals of Mathematical Statistics , 31(3):643-655, 1960. 1, 2, 3

Remi M Sakia. The box-cox transformation technique: a review. Journal of the Royal Statistical Society Series D: The Statistician , 41(2):169-178, 1992. 1

Ghazal Shabestanipour, Zachary Brodeur, William H Farmer, Scott Steinschneider, Richard M Vogel, and Jonathan R Lamontagne. Stochastic watershed model ensembles for long-range planning: Verification and validation. Water Resources Research , 59(2):e2022WR032201, 2023. 2, 3

Richard J Smith. Logarithmic transformation bias in allometry. American Journal of Physical Anthropology , 90(2):215-228, 1993. 3

Craig A Stow, Kenneth H Reckhow, and Song S Qian. A bayesian approach to retransformation bias in transformed regression. Ecology , 87(6):1472-1477, 2006. 3

Bogdan Strimbu. Correction for bias of models with lognormal distributed variables in absence of original data. Annals of Forest Research , 55(2):265-279, 2012. 3

Bogdan M Strimbu, Alexandru Amarioarei, John Paul McTague, and Mihaela M Paun. A posteriori bias correction of three models used for environmental reporting. Forestry: An International Journal of Forest Research , 91(1):49-62, 2018. 1, 3

Jie Sun, Zhaoying Ding, Xiaoshuang Chen, Qi Chen, Yincheng Wang, Kaiqiao Zhan, and Ben Wang. CREAD: A classification-restoration framework with error adaptive discretization for watch time prediction in video recommender systems. In Michael J. Wooldridge, Jennifer G. Dy, and Sriraam Natarajan, editors, Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada , pages 9027-9034. AAAI Press, 2024. 1, 3, 6, 7, 8, 22, 27, 29, 30

Ali Vanderveld, Addhyan Pandey, Angela Han, and Rajesh Parekh. An engagement-based customer lifetime value system for e-commerce. In Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi, editors, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016 , pages 293-302. ACM, 2016. 3

Xiaojing Wang, Tianqi Liu, and Jingang Miao. A deep probabilistic model for customer lifetime value prediction. arXiv preprint arXiv:1912.07753 , 2019. 1, 3, 6, 8, 22, 27, 29

Yunpeng Weng, Xing Tang, Zhenhao Xu, Fuyuan Lyu, Dugang Liu, Zexu Sun, and Xiuqiang He. Optdist: Learning optimal distribution for customer lifetime value prediction. In Edoardo Serra and Francesca Spezzano, editors, Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 , pages 2523-2533. ACM, 2024. 1, 3, 6, 8, 22, 23, 27, 29

Shen Xin, Martin Ester, Jiajun Bu, Chengwei Yao, Zhao Li, Xun Zhou, Yizhou Ye, and Can Wang. Multi-task based sales predictions for online promotions. In Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu, editors, Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019 , pages 2823-2831. ACM, 2019. 1, 3

Mingzhe Xing, Shuqing Bian, Wayne Xin Zhao, Zhen Xiao, Xinji Luo, Cunxiang Yin, Jing Cai, and Yancheng He. Learning reliable user representations from volatile and sparse data to accurately predict customer lifetime value. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Data Mining , pages 3806-3816, 2021. 3, 7

Borui Ye, Shuo Yang, Binbin Hu, Zhiqiang Zhang, Youqiang He, Kai Huang, Jun Zhou, and Yanming Fang. Gaia: Graph neural network with temporal shift aware attention for gross merchandise value forecast in e-commerce. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) , pages 3320-3326. IEEE, 2022. 1, 3

Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang, and Kun Gai. Deconfounding duration bias in watch-time prediction for video recommendation.

In Aidong Zhang and Huzefa Rangwala, editors, KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , pages 4472-4481. ACM, 2022. 3, 29

Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, and Ji-Rong Wen. Counteracting duration bias in video recommendation via counterfactual watch time. In Ricardo Baeza-Yates and Francesco Bonchi, editors, Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, August 25-29, 2024 , pages 4455-4466. ACM, 2024. 3

Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, and Ji-Rong Wen. Uncovering user interest from biased and noised watch time in video recommendation. In Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso Di Noia, Justin Basilico, Luiz Pizzato, and Yang Song, editors, Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023 , pages 528-539. ACM, 2023. 3

Yu Zheng, Chen Gao, Jingtao Ding, Lingling Yi, Depeng Jin, Yong Li, and Meng Wang. DVR: micro-video recommendation optimizing watch-time-gain under duration bias. In João Magalhães, Alberto Del Bimbo, Shin'ichi Satoh, Nicu Sebe, Xavier Alameda-Pineda, Qin Jin, Vincent Oria, and Laura Toni, editors, MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022 , pages 334-345. ACM, 2022. 3

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , KDD '18, page 1059-1068, New York, NY, USA, 2018. Association for Computing Machinery. 29

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Yes. Our main contributions are also detailed in Sec. 1. Also see Sec. 3, Appendix B for more theoretical evidence and Sec. 4, 5 for more experimental evidence.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Yes. We discussed the limitations of our work in Sec. 6.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: We detail the model assumption in Sec. 3.3 and proof of theoretical unbiasedness in Sec. 3.2, Appendix B.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: Regarding the data, the codes for producing the synthetic data is presented in Code 2, and for real-world ones two publicly-accessable datasets (CIKM16, DTMart) are adopted for reproducibility, as detailed in Appendix D.1.1, D.2.1. Regarding the implementation, all experimental details for synthetic and real-world data are presented in Appendix D.1.2, D.2.3, respectively.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: Regarding the data, we provide the codes for producing the synthetic data in Code 2, and for the real-world data scenario, we adopt two publicly-accessible datasets (CIKM16, DTMart). And we'll open-source our code of our method implementation.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines (https://nips.cc/public/guides/ CodeSubmissionPolicy) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https: //nips.cc/public/guides/CodeSubmissionPolicy) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We detail the experimental settings for synthetic and real-world data in Appendix D.1.2, D.2.3, respectively.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: We report statistical significance in the key results, including Table 1, 3, 9, 6.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We include the related information in Appendix D.1.2, D.2.3.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?

Answer: [Yes]

Justification: We have read the NeurIPS Code of Ethics and made sure that the paper conforms to it.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: As discussed in Appendix F, though our method has been applied in two core recommendation scenarios on a leading e-commerce platform (DAU > 300M) to serve the major online traffic, there are no obvious paths that lead to potential harm since our work just present a unbiased regression methodology and is not tied to particular applications.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: We do not foresee any high risk for misuse of this work.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: Yes, we credited them in appropriate ways.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: This paper does not release new assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.

A Frequently Asked Questions (FAQ)

A.1 Root reason for the trade-off between point accuracy (e.g. NRMSE) and XAUC

Here we elaborate on the root cause of the trade-off phenomenon between point accuracy and XAUC, and further highlight the limitations of XAUC. This limitation leads to the inconsistency between the offline XAUC metric and online business metrics (as shown in Table 9), making XAUC no longer suitable for offline assessment. To address this, we supplemented the offline evaluation with two more consistent offline metrics, high-value XAUC (as shown in Table 8) and a commonly used ranking metric NDCG (as shown in Table 11), on both of which our model demonstrates superior performance. For clarity, let us start with a concrete case as follows.

An illustrative case for trade-off: We first sample two inputs { x 1 , x 2 } ∼ P (X) , and then sample 3 groups of target y from P (Y | x 1 ) and P (Y | x 2 ) , respectively, and let the samples be { 3 , 3 , 3 } ∼ P (Y | x 1 ) and { 8 , 1 , 1 } ∼ P (Y | x 2 ) , and let T( x ) = log( x ) . Then, 1) The LogMSE prediction is f ( x 1 ) = T -1 ( E [T(Y) | x 1 ]) = exp((log(3) + log(3) + log(3)) / 3) = 3 , f ( x 2 ) = T -1 ( E [T(Y) | x 2 ]) = exp((log(8) + log(1) + log(1)) / 3) = 2 , where f ( x 1 ) > f ( x 2 ) and the bias occurs on the sample X = x 2 ; 2) The TranSUN/GTS prediction is f ( x 1 ) = E [Y | x 1 ] = (3 + 3 + 3) / 3 = 3 , f ( x 2 ) = E [Y | x 2 ] = (1 + 1 + 8) / 3 = 10 / 3 , where f ( x 1 ) < f ( x 2 ) ; 3) The descending ranking result of LogMSE is { 3 , 3 , 3 , 8 , 1 , 1 } , with XAUC = 0.333, NRMSE = 0.324, and TRE = 4/15, while the result of TranSUN/GTS is { 8 , 1 , 1 , 3 , 3 , 3 } , with XAUC = 0.233, NRMSE = 0.301, and TRE = 0; 4) It is observed that a trade-off occurs between point accuracy (NRMSE, TRE) and XAUC, i.e. TranSUN/GTS achieves better point accuracy (NRMSE, TRE) while XAUC worsens.

Root cause of the trade-off: 1) Reason for the improvement of high-value XAUC & overall point accuracy metric : TranSUN/GTS compensates underestimation brought by retransformation bias, resulting in better point and rank accuracy of high-value samples ( e.g. for sample ( x 2 , 8) , relative error improves from 0.75 to 0.58, and its rank order moves from 4th to 1st, consistent with results on Indus Top (Table 8)), which significantly improves the overall point accuracy ( e.g. improvements in NRMSE & TRE in the above case) since these metrics are value-aware ( i.e. metric calculation aware of absolute value of sample label); 2) Reason for the drop of overall XAUC : Correcting retransformation bias also leads to some hard low-value samples being overestimated ( e.g. predictions for the two samples ( x 2 , 1) increase from 2 to 10/3, consistent with Fig. 3c), which dominates the overall XAUC drop since there are far more hard low-value samples than high-value ones in highly right-skewed data and XAUC metric is value-agnostic ( i.e. metric calculation only aware of the relative rank order of sample label but not aware to its absolute value).

Overall XAUC is inconsistent with online metrics, thus we use high-value XAUC and NDCG:

1) The core goal of a recommender system is to improve online business metrics , while XAUC is merely an intermediate metric. For business target modeled by regression (GMV, watch time, etc. ), the event of ranking high-valued samples in the correct position is far more important for online metrics than that of low-valued samples. However, these two events are equally weighted (i.e. value-agnostic) in the calculation of XAUC, thereby being dominated by abundant low-valued samples when data are highly right-skewed, which probably contribute only a small portion of online metrics ( e.g. 70% of the top low-valued samples contribute only 20% of GMV in Indus). Therefore, overall XAUC is no longer consistent with online metrics; 2) To address this, we supplemented the offline assessment with a more consistent metric, high-value XAUC (Table 8), based on which we achieved improved online results (Table 9). Additionally, NDCG also addresses XAUC's limitation, so we also provide their results. As shown in Table 11, NDCG is computed within all samples (NDCG@All) and the top 10% high-value ones (NDCG@10%), respectively. Key observations include: a) TranSUN performs better in NDCG@All than in XAUC since NDCG is more robust to low-value samples. b) TranSUN shows superior performance in NDCG@10%, consistent with the reasons described in the 2nd point.

A.2 Convergence analysis for L T sun , L T MSE , and L MSE

As for convergence rate bound, since their loss formulations are all squared errors, the theoretical convergence rate bound using SGD is O ( 1 √ K ) under the Lipschitz assumption, where K is update iteration number, according to [6]. The key difference in the convergence performance of L T sun , L T MSE ,

and L MSE lies in their smoothness . This is because the variance and the outlier proportion of the target are different, resulting in optimization landscapes with different degrees of smoothness. As shown in Table 12, we present the target variance ( Var ), the variance divided by squared expectation ( V/E2 ), and the outlier proportion ( Outlier% ) outside of 3 times the standard deviation of the three losses, where T( x ) = log( x ) . As shown, L T sun has a smaller variance and a better outlier distribution than the vanilla MSE, thus L T sun converges more smoothly (Fig.1). Therefore, TranSUN/GTS essentially decomposes a difficult task into two easier and smoother tasks.

A.3 Efficiency analysis for the proposed TranSUN

As different parameter sharing architectures of TranSUN are explored in Appendix E.4, here we supplement the results (Table 16) with TranSUN's additional memory (Mem) and computational overhead (MFLOPS), and calculate the relative increase (OH) compared with T-MSE. As shown in Table 13, the only-sharing-embedding architecture scheme is the most cost-effective, maintaining optimal performance while introducing much little overhead. On the other hand, we provide additional information on the resource overhead associated with the online TranSUN model in the e-commerce platform's recommendation system, which achieves a high ROI measure (with Table 9): 1) Training: Under the same resources (800 workers), the update iteration (global step) number per second of LogMSE and TranSUN were 89.2 and 86.8, respectively. Therefore, applying TranSUN only reduces the training speed by 2.7%, with the training time increased by 2.78%; 2) Inference: The mean and p99 latency of the LogMSE were 27.8ms and 49.8ms, respectively, while the mean and p99 latency of TranSUN were 28.1ms and 51.6ms, respectively; 3) Deployment: In the online service with 300 million daily active users, deploying TranSUN did not increase CPU resources and only increased GPU resources by 5%.

A.4 Optional techniques for optimizing LogSUN's online performance

As mentioned in Appendix A.1, TranSUN likely results in a certain decrease in the overall XAUC, particularly for low-priced orders. Although these orders only contribute 20% of the total GMV, they represent the majority in view of the total number of transaction orders. Therefore, the reduced

ranking accuracy of these orders' GMV estimates deteriorates the accuracy of their overall ranking score, which in turn deteriorates the online metrics regarding the number of transaction orders. Therefore, it is crucial to lift the ranking accuracy of these samples under TranSUN's modeling. Here are two techniques that have been validated as effective in our scenarios: 1) Selecting a suitable combination of ranking losses: As shown in lines 77-79 of the Code 1, selecting these two ranking losses carefully has been shown to improve the overall XAUC by > +0.01 on our private industrial data. 2) Fusion with the original predictions: As shown in lines 51-52 of the Code 1, a pre-defined fusion threshold can be used to restrict TranSUN's functioning scope to high-priced samples, which improved the overall XAUC by > +0.003 on our private data. To select the threshold, we first divided the sample into 50 bins with equal frequency based on their original prediction values in ascending order. Then, we calculated the XAUC results of the original and debiased prediction values within each bin, respectively. We found that as the bin number increased, the XAUC gain of debiasing gradually shifted from negative to positive. Therefore, the left boundary of the bin that first achieved a positive XAUC gain was adopted as the fusion threshold. Once this threshold is determined, it does not need to be adjusted anymore, unless the data distribution shifts significantly.

A.5 Comparing GTS with probabilistic modeling methods (PMM), e.g. Mixture Density Networks (MDN), Normalizing Flows (NF)

A comprehensive comparison is shown in Fig. 4. Specifically, when our GTS serves as a point estimator, it has three advantages over PMM: 1) Flexibility: It can be flexibly plugged into any model with minor revision (as validated in Table 6). Another advantage is that in industrial practice TranSUN can be fully warmed-up from the online base model, further accelerating TranSUN's convergence; 2) Lightweight: It requires much less resource than PMM. For example, MDN's overhead grows linearly with mixture components, and NF incurs significant overhead due to Jacobian computations; 3) Training stability: PMMis much harder to converge than GTS given their complex assumptions. For example, on Indus, OptDist [41] (an MDN case) needs 2.13x the iterations TranSUN requires to reach batch-NRMSE= 3 . 0 .

In addition, with minor revision to GTS's model assumption ( i.e. modeling constant σ in Eq. (11) as a function of x ), GTS can also directly model the conditional probability P (Y | x ) in the CTM manner [16]. Specifically, GTS's assumption formulation turns to

where s ( x ; θ s ) > 0 actually models the reciprocal of the standard deviation σ ( x ) . In training stage, according to [29, 16], one can establish the scoring-rule-based loss L sr ( x, y ; h ) as

where the 1 {·} is the indicator function, { v i } N i =1 denotes pre-defined ascending grid points of y , Φ( u ) = 1 2 [1 + erf( u √ 2 )] , and h ( x, y ) is defined as

Therefore, the total loss is formulated as

MSE

MDM

T-MSE

NF

TranSUN

GTS

CTM

CLTM

Unbiasedness

Convergence

Overhead

Pluggability

Holds all the time

Depends on some designs

(e.g.

𝑃

!

and

ℎ(⋅)

in NF&CTM)

Barely holds (e.g.

T

in

T-MSE is barely linear)

Fast and smooth on

skewed data

Occasionally unstable

due to complex design

Unstable

on skewed data

For any regression model

For specific models (e.g.

TranSUN

is for T-MSE)

Not pluggable

Minimal

Large in some cases (e.g.

MDN's

𝐾

grows too much)

Often significant (e.g.

Jacobian computation in

NF)

+

☆★★

☆★☆★

★★☆☆

★★★★

Apply T to y

Alter obj:

Alter obj:

maxPy)x(3|z) →max Py)(y < |x)

Specify h(-) and P

-f(x)+y|x=~N(0,o²)

-f(x)+T(y)|x =2~ N(0,o²)

h（y丨x，x)=~P

h（y丨x，x)=2~P

K = 1,

Specify h(-) as linear,

21~N(f(x)，o²)

P as Gaussian

★★

★★☆★

Q =E, x(x) = 1/(IT-(x) + e)

Alter obj:

Replace α(z) with s(Qy(zx), and omit T(-)

-f(x)+

[T-(BY[T(y)])|+∈

（00）N~=

-f(x)+(Qyx[T(y)])-y丨x =2~ N(0,o²)

-f(x) +α(x) · T(y)|x = ~ N(0, 1)

K

π（x)p=（3|x)

M>

K=1

s.t,

πk（x)=1，

π-(x)≥0

In inference stage, our GTS models the conditional probability with

A.6 Principled guidelines for utilizing GTS

The principled guideline is to align the model assumption (Eq.(11)) as closely as possible with the real data, though this requires manual trial-and-error. Our experience is to first fit the data solely using a mainstream regression model as Q , then analyze the variance, skewness, kurtosis, and outlier distribution of Y ∗ κ ( Q ) to select a suitable κ form, which determines the final GTS model. This significantly reduces training resources when tuning κ . A more general methodology is left for future work, as stated in the conclusion section.

B Mathematical Details in Our Approach

B.1 Derivation of transformed MSE's estimation

The loss function of transformed MSE is exhibited in Eq. (2). Minimizing the loss can result in that

Therefore, f ( x ; θ ) is actually an estimate of E y ∼P (Y | x ) [ T( y ) ] .

B.2 Detailed derivation for TranSUN's estimation

A brief derivation for TranSUN's estimate is exhibited in Eq. (7, 8). Following Eq. (17), we present detailed derivation for TranSUN's estimate ˆ y | x as

B.3 Detailed derivation for estimation from the model with S1 scheme

As mentioned in Sec. 3.2 and Table 4, the S1 bias modeling scheme formulates the bias learning loss as

Following Eq. (18), we present detailed derivation for its estimate ˆ y | x as

As shown above, the model prediction ˆ y | x is actually an estimate of 1 / E y ∼P (Y | x ) [ 1 y ] , which is biased.

B.4 Detailed derivation for GTS's estimation

Owing to the stop _ grad operation, the optimization of the total loss L GTS can be decomposed into two independent parts, as

where L H q is the conditional point loss and L κ LTM is the linear transformation loss. The first term min θ q L H q learns a conditional point according to Eq. (10), which is independent of z ( x ; θ z ) . The second term derives the GTS's estimate ˆ y | x via

B.5 Discussion for the theoretical correctness of the unbiasedness metrics (TRE and MRE)

Details are shown in Appendix D.2.2.

B.6 Utilizing GTS for direct probabilistic modeling

Details are shown in Appendix A.5.

C Challenges of applying external bias correction approaches in industrial recommender systems

Applying external debiasing paradigms from prior works to real-world recommender systems necessitates the development of an additional online post-processing pipeline after the ranking model's scoring pipeline in the overall online system, which suffers from practical challenges including high development costs, substantial maintenance difficulties, and significant system resource consumption. In addition, since the online calibration pipeline [10] has been commonly utilized in many large-scale recommender systems, an alternative approach is to just let this pipeline itself fit the correction. However, in practice, when the retransformation bias problem becomes severe ( e.g. TRE is about 0.6 before calibration), this alternative approach is prone to yield unstable online TRE results after the calibration, which are significantly worse than those of our TranSUN method, with the mean being 180% and the standard deviation being 150% of ours. The instability of calibration results might be caused by the excessively large differences between predicted values and true values, which can even affect the robustness of the calibration pipeline. Therefore, as a practical solution, we propose TranSUN and Generalized TranSUN (GTS), an end-to-end in-model correction method to address the challenges of existing approaches and provide robust model performance of unbiasedness.

D Details in Experimental Setting

D.1 Detailed setting on synthetic data

D.1.1 Data and metric

We adopt 8 distributions H (Y) from three categories: right-skewed, left-skewed, and symmetric. Specifically, we select some classic exponential family instances ( e.g. Gamma, Beta, Normal), multimodal distributions ( e.g. bimodal uniform), and industry-prevalent zero-inflated distributions ( e.g. ZILN [40] with positive rate p + = 0 . 2 ). Detailed implementations are shown in Code 2. For each distribution, we randomly sample N = 10 6 instances. Fig. 2 illustrates the density and some statistics of the sampled P s (Y | x ) . We adopt SRE (Signed Relative Error) as the metric, i.e. (ˆ y -y ) /y where ˆ y and y are prediction and ground truth, respectively.

D.1.2 Implementation details

We implement all methods using the Tensorflow toolbox on an M2 Pro CPU with 16 GB. Since the model input x is fixed in P s (Y | x ) , we directly set it to 1 . 0 . We also implement some advanced classification-based models [23, 38, 21, 9] for comparison, where only their main classification losses are retained. For TPM [23] and CREAD [38], bin number is set to 4 , which is produced via equal-frequency and EAD binning method [38], respectively. MDME [21] adopts an equalwidth binning method to determine 2 sub-distributions, each containing 2 buckets. WLR [9] is only evaluated on RS-ZIG since it requires substantial zero values as negative samples. We also implement ZILN [40] and OptDist [41], which discard classification branches when not evaluated on RS-ZIG. And OptDist is set to adopt 3 SDNs. We set batch size to 1024 and select the optimal learning rate from { 10 -i } 1 ≤ i ≤ 3 ∪ { 5 × 10 -i } 1 ≤ i ≤ 4 . All models are trained for 1 epoch, with each training repeated 10 times with different random seeds to obtain statistical significance, where the p -value is calculated by the t-test.

D.2 Detailed setting on real-world data

D.2.1 Datasets

We adopt two public datasets ( CIKM16 , DTMart ) and one exclusive industrial dataset ( abbr. Indus ). Specifically, CIKM16 aims to predict the dwell time for each session in online searching. We only use the items in the session as input features. The CIKM16 dataset contains 310,302 sessions and 122,991 items. DTMart aims to predict the transaction amount for each order in marketing activities. We use item ID, categories, time taken for procurement, and delivery as input features. The DTMart dataset contains 985,033 orders and 15,546 items. Indus aims to predict the Gross Merchandise Value (GMV) of each order paid in the recommendation scenario of an anonymous e-commerce platform. The Indus dataset contains 132,657,136 orders, 14,538,759 items, and 67,729,869 users. CIKM16 and DTMart datasets are both randomly divided into training and testing samples at a ratio of 4:1, and the Indus dataset is divided at a ratio of 5:1. Interestingly, we found that the Pareto phenomenon occurred in the Indus dataset, where 80% of the GMV was contributed by the top 30% high-priced orders. Consequently, we further conduct evaluation solely on the top high-priced orders in the testing set, abbr. Indus Top test, to better analyze model performance.

D.2.2 Metrics for assessing unbiasedness

As mentioned in Sec. 5.1, we propose Total Ratio Error (TRE) and Mean Ratio Error (MRE) to assess unbiasedness. These metrics are formulated as

where ˆ y i is prediction, y i is ground truth, and M is sample number.

Firstly, we theoretically prove that achieving either the optimal TRE or MRE metric is a necessary condition for the model to be unbiased. Specifically, through the definition of the model's unbiasedness, we can derive that

where ˆ y ( x ) = ˆ y | x . For the TRE metric, optimizing it means that minTRE

where the second row in Eq. (26) is derived from the definition of TRE using expectation formulation, and the last row comes from Eq. (25). Regarding the MRE metric, optimizing it implies that minMRE

where the second row in Eq. (27) is derived from the definition of MRE using expectation formulation, and the last row comes from Eq. (25). Notably, the derivation in Eq. (27) explains the reason that the model prediction ˆ y ( x ) is placed in the denominator position of the metric. Specifically, once the MRE is modified to swap the numerator and denominator of each item in it, as

then this new metric is no longer suitable for measuring the model unbiasedness, which actually turns to a necessary condition for the estimation of 1 / E y ∼P (Y | x ) [ 1 y ] , as

where the second row in Eq. (29) is derived from the definition of MRE (wr) using expectation formulation.

Next, we theoretically prove that achieving the optimal NRMSE metric is a necessary and sufficient condition for the model to be unbiased. Specifically, NRMSE is defined as

and thus optimizing it means that minNRMSE

where the second row in Eq. (31) is derived from the definition of NRMSE using expectation formulation, and the last row indicates that ˆ y ( x ) is an unbiased estimate.

Discussion on not using NRMSE as the primary metric for observing unbiasedness. Since NRMSE is a necessary and sufficient condition for a model to be unbiased, intuitively it should be preferred over TRE and MRE as the primary metric for observing unbiasedness. However, the NRMSE metric has a significant issue, that its optimal value ( i.e. the minimum) is unknown. This problem hinders us from intuitively perceiving the presence and severity of the bias, making it much difficult to verify the capability of different models in model iterations towards eradicating bias. In contrast, although TRE and MRE are both necessary conditions for a model to be unbiased, their optimal values ( i.e. the minimum) are known, which is 0 . 0 . Therefore, they can both clearly indicate whether a model is biased. In our experiments, we take the approach for analyzing unbiasedness where TRE and MRE are initially compared to determine if the model is biased, and then NRMSE is subsequently compared to identify the model with the best unbiasedness. The idea of the analytical approach above is also reflected in our result reporting. For example, NRMSE results are not reported in Table 3 and Fig. 3b, 3c, 3d, which is because the gaps among methods in TRE and MRE are both quite pronounced already.

D.2.3 Implementation details

For public datasets, all methods employ an 'Embedding+MLP' architecture, where continuous features are discretized, each feature's embedding dimension is set to 16 , and the MLP has 4 layers with hidden dimensions of 123 , 64 , and 32 . For Indus, all methods employ a DIN architecture [49], where continuous features are also discretized, and the MLP has 4 layers with hidden dimensions of 1024 , 512 , and 256 . The bias modeling branch of TranSUN defaults to non-shared parameters on public datasets and shares the embedding layer on Indus. The default setting for ϵ is 1 . 0 . For other baselines, WLR [9] folows the implementation in [45]. ZILN [40] and OptDist [41] both discard classification branches. The number of bins in TPM [23] and CREAD [38] is set to 16 . MDME[21] adopts 4 sub-distributions, each containing 4 buckets, and OptDist adopts 3 SDNs. The equal-frequency binning method is adopted for both TPM and MDME, while the EAD method is adopted for CREAD. On public datasets, the SGD optimizer is utilized with a batch size of 512 and a learning rate of 0 . 001 . On the Indus, the AdagradDecay optimizer is employed with a batch size of 64 and a learning rate of 0 . 01 . All models adopt only one training epoch, and each training is repeated 5 times with different random seeds to obtain statistical significance, where the p -value is calculated by the t-test. We implement all methods using the Tensorflow toolbox. The model training on public datasets is performed on an M2 Pro CPU with 16GB, while the training on the Indus dataset consumes resources including 2000 CPUs with 90 GB. In addition, the statistical significance in the results of the online A/B test (Table 9) is calculated by the z-test.

E More Experimental Results

E.1 Biasedness comparison of TPM with different bin numbers on synthetic data

As shown in Table 2, TPM [23] underestimates the mean on left-skew and overestimates it on right-skew data, likely because it learns the average of quantiles while the bin number is only 4 . The bin number is set so small to amplify the theoretical biasedness problem of TPM, which can be

significantly alleviated by increasing the bin number, as shown in Table 14. This conclusion also applies to CREAD [38].

E.2 Details for online A/B test

In Taobao App (DAU > 300M), our LogSUN model is deployed as GMV prediction models in both product and short video recommendation scenarios in Guess What You Like domain in the homepage, which serve as pointwise ranking models for GMV target in the ranking stage of the recommender systems to optimize object distribution. Notably, in the short video recommendation scenario on the e-commerce platform, all the short videos are product description videos, each of which links to a certain product, therefore, the GMV of a short video actually refers to the transaction value of the product described in the video after the short video is clicked.

In the A/B test, our LogSUN is compared against the previous online base (LogMAE) and the commonly used LogMSE. Two common metrics, GMV and GMVPI (GMV Per Impression), are adopted to assess online performance. Due to the large variability of these GMV-related metrics, we allocated either substantial traffic or a sufficiently long period for A/B tests to obtain statistically significant results. Specifically, we conduct two A/B tests for one week in the product recommendation scenario, with LogSUN serving 15% of the total traffic, LogMAE 20%, and LogMSE 9%. And we conduct one A/B test for one month in the short video recommendation scenario, with LogSUN serving 6% of the total traffic, and LogMAE 6%.

As shown in Table 9, LogSUN achieves significant gains ( e.g. > +1.0% GMV gains) over the baselines in both recommendation scenarios. We attribute the consistent gains in online metrics to the following two reasons. The first reason is that LogSUN significantly improves the predictive accuracy of samples in each target bin by eradicating the retransformation bias, as shown in Figure 3c. The second is that LogSUN empirically improves the ranking accuracy of the top high-priced samples ( e.g. the improvements in AUC on Indus Top test, as shown in Table 8), which contribute 80% of the total GMV though they are small in number. On the other hand, we observed that the GMV gain in the short video recommendation scenario was much larger than that of the product. This is possibly because the retransformation bias problem is much more severe in the short video recommendation scenario (TRE is about 0.6352, nearly 140% of the product's). Now, LogSUN has been deployed in the two core recommendation scenarios to serve the major traffic.

E.3 Comparison of GTS instance models on synthetic data

Some common L H q and κ are selected to devise different instances of GTS, providing reference results for its application. As exhibited in Table 15, in terms of L H q , MSE loss is the most versatile loss as it fastly converges to the unbiased solution in all distributions, whose Q corresponds to the arithmetic mean point. Among other losses, MAE presents better convergence on right-skewed data, while MSPE and MAPE both converge more effectively on left-skewed and symmetric data. Regarding function κ , all candidates exhibit unbiasedness consistent with TranSUN. The performance of κ on real-world data is shown in Table 10.

E.4 Architectural sensitivity on real-world data

Since our method introduces an extra bias learning branch to transformed MSE, we explore the impact of different parameter sharing schemes between the added branch and the original model, which is conducted on the LogSUN model. In Table 16, sharing parameters from bottom layers ( e.g. embedding layers) has minimal impact on performance, whereas further sharing more parameters

within MLP results in a noticeable performance degradation, which might be inferred that the added task primarily affects the downstream MLP-modeled fitting function rather than the distribution of latent embedding.

E.5 Comparison of GTS instances with different κ functions on real-world data

As shown in Table 10, compared to the one used in TranSUN, other κ candidates all bring about poorer loss convergence, which is probably because the variances of their κ · y are all too large (Table 17), leading to an excessively steep optimization landscape for θ z .

F Broader Impacts

We think that there is no societal impact of the work performed. Though our method has been applied in two core recommendation scenarios on a leading e-commerce platform (DAU > 300M) to serve the major online traffic, there are no obvious paths that lead to potential harm, since our work just presents an unbiased regression methodology and is not tied to particular applications.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel preemptive paradigm called TranSUN to address the long-neglected retransformation bias problem in regression models for recommender systems. TranSUN introduces a joint bias learning approach, ensuring theoretically guaranteed unbiasedness and superior empirical convergence. The paper also generalizes TranSUN into a generic regression model family, Generalized TranSUN (GTS), which offers more theoretical insights and serves as a flexible framework for developing various bias-free models. Extensive experiments demonstrate the superiority and broad applicability of the proposed methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This paper is clearly written and well organized.\n- The retransformation bias is a crucial problem but neglected by most recommendation methods, so the motivation of this paper is interesting. \n- The experimental results are promising.\n\nConcerns:\n- In recommendation tasks, we often use pairwise or listwise ranking metrics (e.g. XAUC adopted by authors) for evaluation.  However, the function T can only affect the labels y in a pointwise manner (Eq.(1) in this paper), but not change their relative ranking orders( E[sign(y_1>y_2)] <=> E[sign(T(y_1)>T(y_2)] ). In this way, I wonder how the retransformation bias affect the model performance w.r.t the ranking metrics. I hope the authors could provide some theoretical justification on this point. Otherwise, we can reasonably speculate that the improvement on AUC does not stem from the proposed strategy, and thus its application scope might be limited.\n- By introducing T(y), the model can adapt the high-skewed distribution of y, but this operation leads to a Jensen gap (Eq.(3)). To fill this gap, the author proposes Eq.(9). However, the optimization of L_{sun} in Eq.(9) might also be affected by high-skewedness of y in turn. This seems to be a paradox.\n\n-----!!!\n\nIn the rebuttal phase, the response from the authors have resolved all my concerns above. Therefore, I intend to improve the rating of this paper.\n\nQUESTIONS:\n- How proposed method performs w.r.t other ranking metrics (such as NDCG .etc)? \n- How to theoretically explain the improvement on pairwise/listwise ranking metrics?\n- How to resolve  the aforementioned paradox between the high-skewed distribution and retransformation bias?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on addressing the **re-transformation bias** issue in regression models for recommendation systems. This problem arises when functions like the logarithm are used to transform target values with skewed distributions (e.g., GMV) to stabilize training. When the model's predictions are transformed back to the original scale, a systematic bias is introduced. The authors propose a \"proactive\" method called **TranSUN**, which introduces a jointly trained auxiliary model branch to learn and correct this bias from the ground up, thereby theoretically guaranteeing an unbiased prediction. This method is further generalized into the **GTS (Generalized TranSUN)** framework, providing a universal approach for designing unbiased regression models. The paper validates the effectiveness of its method through synthetic data, public datasets, and a large-scale online A/B test on a leading e-commerce platform.\n\nSTRENGTHS AND WEAKNESSES:\n**Pros:**\n\n1. **Addresses a Practical Problem:** The paper accurately identifies and solves a concrete technical problem prevalent in the industry (especially in e-commerce, video streaming, etc.). Eliminating systematic bias has clear business value for scenarios requiring precise prediction of business metrics (e.g., GMV, user watch time). The paper introduces an auxiliary branch to learn the bias, supervised by a multiplicative bias modeling scheme aimed at capturing the ratio between predicted and true values. This approach tackles the model performance degradation caused by right-skewed data distributions in real-world applications and provides a detailed theoretical foundation.\n2. **Exhaustive Experimental Validation:** The experimental work in this paper is very solid, particularly the evaluation on large-scale industrial datasets and the successful deployment in an online A/B test. This directly proves the method's utility and effectiveness in specific business scenarios. This is the most commendable part of the paper. The method is not limited to e-commerce platforms and can be applied to recommendation systems in various other domains, showing its broad practical potential.\n\n**Cons:**\n\n1. **Limited Research Paradigm and Problem Significance:** Although re-transformation bias exists, it is fundamentally a product of the traditional \"predict-then-rank\" paradigm. Compared to concurrent cutting-edge research in recommendation systems, this paradigm itself is being challenged. Much of the frontier work is shifting towards **end-to-end Learning-to-Rank** frameworks that directly optimize ranking metrics (like NDCG). These frameworks may not rely on precise point estimates of continuous values from the outset, thus naturally circumventing the re-transformation bias problem. From this perspective, the paper's effort to patch a problem within a specific paradigm makes its significance and novelty relatively limited.\n2. The study primarily relies on TRE and MRE as core unbiasedness metrics (§5.1), justifying their necessity through the derivations in Appendix C.2.2 (Eq. (21)-(22)). However, these metrics are necessary but not sufficient conditions for unbiasedness: even if they reach their optimal value (zero), it does not guarantee the absence of bias across all data subgroups. For instance, systematic prediction errors might persist in local data distributions undetected by TRE/MRE.\n3. **Limited Methodological Innovation:** The core idea of TranSUN—introducing an auxiliary network to learn a correction term—is not uncommon in the broader machine learning field. It can be seen as a direct application of residual fitting, the **boosting idea** from ensemble learning, or an auxiliary task that provides corrections for a primary task in multi-task learning. The authors have skillfully applied it to the specific problem of re-transformation bias, but its methodological originality is relatively modest.\n4. **At the Cost of Core Ranking Performance:** This is one of the paper's weaknesses. The paper explicitly states that while the method improves the accuracy of point estimates, it harms the core task of the recommendation system—its ranking ability (as indicated by the drop in the XAUC metric). The authors acknowledge this trade-off but fail to provide an effective solution or an in-depth analysis of its causes, which may make the method unacceptable in the vast majority of recommendation scenarios where ranking performance is the priority.\n5. **Gap with Frontier Probabilistic Modeling Methods:** Compared to contemporary research, the paper's reliance on a \"transform-correct\" method appears somewhat conservative. Currently, using deep probabilistic models like **Mixture Density Networks (MDNs) or Normalizing Flows** to directly model the complex, skewed raw data distribution p(y|x) has become a significant research direction. These methods can directly and unbiasedly compute the conditional expectation E[y|x] and provide richer probabilistic information (such as uncertainty), fundamentally avoiding the re-transformation problem. In contrast, TranSUN/GTS remains a point estimation model, lagging behind these advanced methods in terms of model expressiveness and the richness of its output information.\n6. While the paper highlights that TranSUN is deployed on an e-commerce platform with over 300 million daily active users (DAU) (§5.2), it fails to quantify the computational overhead introduced by its key components: the auxiliary branch (Eq. 4) and the dynamic slope (Eq. 9). Crucially, the paper does not provide a comparison of inference latency against baseline methods.\n\nQUESTIONS:\n1. **On the Root Cause of Ranking Performance Degradation:** You mentioned that the decrease in XAUC is an acceptable trade-off. Could you please elaborate on why a method designed for more \"accurate\" predictions leads to a decline in ranking ability? Does this imply that the model, in correcting for large biases on a few high-value samples, sacrifices its ranking discrimination for the majority of samples? Would this limit its general applicability? Additionally, you mentioned that a part of it increased; could you explain what happened there? You state the drop in XAUC is \"acceptable given the improvement on TRE\" (§5.2). Does this imply unbiasedness hurts ranking? Please analyze the correlation between prediction error reduction (TRE) and ranking metrics (XAUC) to clarify this trade-off.\n2. **Comparison with Direct Probabilistic Modeling Methods:** Your method can be viewed as a sophisticated correction to a Gaussian distribution assumption. How do you see it comparing to methods that directly model p(y|x) using more powerful distributional assumptions (e.g., via MDNs or Normalizing Flows)? The latter can obtain unbiased expected predictions in one step without introducing extra tricks like `stop_grad` or auxiliary networks. In your view, what advantages does TranSUN offer over these methods (e.g., in terms of training stability, computational overhead, ease of integration)?\n3. Can you prove that the linear transformation assumption (Eq. 11) holds under non-Gaussian noise or multi-modal target distributions (e.g., RS-BU in Fig. 2)? If not, what are the theoretical guarantees when this assumption is violated?\n4. **Necessity of the GTS Framework:** You generalized TranSUN to the GTS framework. However, the experimental section mainly validates TranSUN and its variants. Beyond theoretical elegance, what unique and validated practical value does the GTS framework offer beyond TranSUN? Is there a specific scenario where standard TranSUN would fail, and one must leverage the GTS framework to design a specific, non-trivial instance to solve the problem?\n5. How much additional training time and computational cost does TranSUN introduce during training? Is this mentioned in the paper? Could you provide an example to give a concrete sense of the scale? Does this impact computational performance and speed in industrial scenarios? The auxiliary branch (Eq. 4) and dynamic slope (Eq. 9) inevitably add computational load. Please quantify: (a) the inference latency (in ms) of TranSUN vs. LogMSE on the Indus dataset, and (b) the extra CPU/GPU resources consumed during online serving at the scale of over 300 million DAU.\n6. Are there principled guidelines for determining the optimal $L_v$ (e.g., MAE for right-skewed data) and $\\Psi$ functions, or is manual trial-and-error the only way? Does this limit the method's applicability in resource-constrained environments?\n\nLIMITATIONS:\nYes. The authors discuss limitations in Section 6. However, I believe the discussion of the core limitation—the degradation in ranking performance (XAUC)—is too understated. The authors attribute it to the \"no free lunch\" theorem but fail to sufficiently explore the severity of this issue. For a paper on recommendation systems, harming the core ranking metric is a very serious problem. This needs to be discussed more explicitly and deeply as a core limitation, with an analysis of how it restricts the method's scope of application.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the retransformation bias problem in regression models, which is especially important in recommender systems where data is highly skewed. The authors propose TranSUN, a preemptive method that eradicates this bias intrinsically during training. TranSUN augments standard model with an auxiliary branch that jointly learns a correction factor, ensuring the final prediction is an unbiased estimate of the target's expectation. The paper further generalizes this idea into the Generalized TranSUN (GTS) framework, which reveals that the core mechanism for unbiasedness is \"conditional linearity\" and provides a flexible approach to debias various regression models. The method's effectiveness is demonstrated on synthetic and real-world datasets, with a successful large-scale deployment in two recommendation scenarios on e-commerce platform.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. **Practical Problem:** The paper tackles retransformation bias, a theoretically well-known but practically critical issue in industrial recommender systems that directly impacts the accuracy of predicted values like GMV or user engagement. \n\n2. **Theoretically Sound Method:** The proposed TranSUN method is simple yet effective, and its claim of unbiasedness is supported by theoretical derivations. The generalization to the GTS framework provides a deeper understanding of the solution space. \n3. **Comprehensive Evaluation:** The experimental validation is quite comprehensive.\n    (1) The use of synthetic data with diverse distributions demonstrates the bias in baselines and the unbiasedness of the proposed method.\n    (2) The experiments on public and a large-scale proprietary industrial dataset show consistent and significant improvements in bias-related metrics (TRE, MRE) and often in standard regression metrics (NRMSE, NMAE).\n    (3) The successful, large-scale online A/B test with significant GMV lift shows promising real-world impact.\n\n### Weaknesses\n1. **Lack of Efficiency Analysis:** The paper introduces an auxiliary model branch, which presumably adds computational overhead. While Table 13 explores architectural choices, it focuses on accuracy, not efficiency. \n\n2. **Limited Discussion on Ranking Performance Trade-off:** The results (e.g., Table 7) show that while the method improves value prediction, it can sometimes lead to a slight decrease in ranking performance (XAUC). The authors acknowledge this as a limitation, but a deeper discussion would be beneficial. For example, why does correcting for value bias sometimes harm the relative ordering of items? The fact that XAUC improves for high-value items on the Indus dataset (Table 8) is an interesting counterpoint that is not fully explored. A more thorough analysis of this trade-off would be valuable.\n\nQUESTIONS:\nSee weakness\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTranSUN introduces a preemptive retransformation bias correction paradigm by jointly training a primary branch that predicts the transformed target and an auxiliary branch that learns a corrective factor, thereby achieving theoretically guaranteed unbiased regression with strong empirical convergence. The approach is further generalized into the GTS framework, enabling plug-and-play loss functions and correction schemes, and demonstrates substantial improvements across synthetic benchmarks, public datasets, and large-scale industrial A/B tests.\n\nSTRENGTHS AND WEAKNESSES:\nS1: The research topic is valuable, as preemptive debiasing is indeed more practical than post-hoc correction methods that require substantial computational resources in industrial recommender systems.\nS2: The motivation behind TranSUN is clearly articulated, and the method is conceptually simple and easy to implement, yet supported by solid theoretical foundations. Moreover, the authors explicitly state the modeling assumptions and provide proofs for unbiasedness.\nS3: Extensive experiments are conducted on synthetic data, public datasets, and large-scale industrial A/B tests, offering solid empirical support for the method's effectiveness.\n\nW1: A key claimed advantage of TranSUN is its strong convergence properties. However, this is only demonstrated empirically, and no formal convergence analysis or theoretical guarantees (e.g., convergence rate bounds) are provided.\nW2: The theoretical guarantee of unbiasedness implicitly relies on the assumption that training and test data are independently and identically distributed (i.i.d.), which is not explicitly stated. In practice, if distribution shift occurs at deployment, the unbiasedness claim may no longer hold.\nW3: Beyond the i.i.d. assumption, achieving unbiasedness requires that the auxiliary model accurately estimates the correction factor (slope model). This implicitly assumes that the learned slope approximates the true ground-truth ratio, which is itself a non-trivial task.\n\nQUESTIONS:\nThe authors observe that while TranSUN improves outcome regression accuracy, it may degrade ranking performance, a phenomenon worthy of deeper investigation. In simplified ranking scenarios (e.g., pairwise comparisons), this reduces to a binary decision task, and prior work has shown that more accurate outcome regression does not necessarily lead to better decision quality. Could the authors elaborate on this point?\n\nLIMITATIONS:\nIn the conclusion, the authors acknowledge key limitations, including the lack of evaluation on non-right-skewed distributions and the observed performance drop in ranking metrics such as XAUC.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-16"}