Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Authors: Chao Cui
Shisong Tang
Fan Li
Jiechao Gao
Hechang Chen
Venue: ICML 2025 poster (2025)
Keywords: Prototype learning
optimal transport
recommendation
Primary area: applications

Abstract: Accurately predicting user watch-time is crucial for enhancing user stickiness and retention in video recommendation systems. Existing watch-time prediction approaches typically involve transformations of watch-time labels for prediction and subsequent reversal, ignoring both the natural distribution properties of label and the \textit{instance representation confusion} that results in inaccurate predictions. In this paper, we propose ProWTP, a two-stage method combining prototype learning and optimal transport for watch-time regression prediction, suitable for any deep recommendation model. Specifically, we observe that the watch-ratio (the ratio of watch-time to video duration) within the same duration bucket exhibits a multimodal distribution. To facilitate incorporation into models, we use a hierarchical vector quantised variational autoencoder (HVQ-VAE) to convert the continuous label distribution into a high-dimensional discrete distribution, serving as credible prototypes for calibrations. Based on this, ProWTP views the alignment between prototypes and instance representations as a Semi-relaxed Unbalanced Optimal Transport (SUOT) problem, where the marginal constraints of prototypes are relaxed. And the corresponding optimization problem is reformulated as a weighted Lasso problem for solution. Moreover, ProWTP introduces the assignment and compactness losses to encourage instances to cluster closely around their respective prototypes, thereby enhancing the prototype-level distinguishability. Finally, we conducted extensive offline experiments on two industrial datasets, demonstrating our consistent superiority in real-world application.

Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Chao Cui 12
Shisong Tang" 12 Fan Li
Jiechao Gao Hechang Chen
Abstract
1. Introduction
The rapid growth of online-video services (e.g. YouTube and Hulu) and video-sharing platforms (e.g. TikTok and Douyin) has driven the increasing demand for personalized and high-quality content (Zhou et al., 2018; Tang al., 2023). In video recommendation systems, user watch-time has become a key metric for measuring user engagement (Covington et al., 2016; Tang et al., 2022; Li et al., 2024). Accurately predicting user watch-time not only helps im- prove user stickiness and retention but also optimizes con- tent distribution and resource allocation, thereby driving the growth of Daily Active Users (DAUs) on the platform (Lin et al., 2023; Zhan et al., 2022; Wu et al., 2024).
Accurately predicting user watch-time is crucial for enhancing user stickiness and retention in video recommendation systems. Existing watch- time prediction approaches typically involve trans- formations of watch-time labels for prediction and subsequent reversal, ignoring both the natural distribution properties of label and the instance representation confusion that results in inaccurate predictions. wo-stage method combining proto- type learning and optimal transport for watch-time regression prediction, suitable for any deep recom- mendation model. Specifically, we observe that the watch-ratio (the ratio watch-time to video duration) within same duration bucket exhibits multimodal distribution. To facilitate incorpo- ration into models, quantized variational autoencoder (HVQ-VAE) to convert the continuous label distribution into a high-dimensional discrete distribution, serving as credible prototypes for calibrations. Based on this, ProWTP views the aligmment between prototypes and instance representations Semi- relaxed Unbalanced Optimal Transport (SUOT) problem, where the marginal constraints of pro- totypes are relaxed. And the corresponding opti- mization problem is reformulated as a weighted Lasso problem for solution. Moreover, ProWTP introduces assignment and compactness losses to encourage instances to cluster closely around their respective prototypes, thereby enhancing the prototype-level distinguishabilityy. Finally, we con- ducted extensive experiments, demonstrating our consistent superiority in real-world application.
Existing methods for Watch-time Prediction (WTP) usually focus on designing specific loss functions or transforming watch-time labels in particular ways to train the model, aiming to improve performance. Weighted Logistic Regres- sion (WLR) (Covington al., 2016) treats WTP task a weighted binary classification problem, approximating the expected watch-time by assigning weights to positive sam- ples. Duration-Deconfoundde Quantile-based (D2Q) model (Zhan et al., 2022) divides videos into different groups based on duration and employs traditional regression within each group estimate the transformed watch-time, Tree-based Progressive Regression (TPM) (Lin et al., 2023) decom- poses WTP into series of ordinal classifications, leveraging tree structure to model conditional dependencies.
hicrarchical vector
However, those methods struggle to consistently maintain high predictive accuracy across different models. They overlook the natural distribution properties labels-we observed that the watch ratio (i.e., the ratio of watch-time to video duration) within the same video duration bucket exhibits a pronounced multimodal distribution, shown captured. in 1()), which has not yet been explicitly Moreover, model trained with watch- time supervision suf- fers from instance representation confusion, as shown in Fig. (bb, making it challenging to accurately differentiate various patterns, consequently, limiting its predictive capa- bility. To address the aforementioned issues, we propose two-stage method called Pro which combines pro- totype learning (Snell et al., 2017; Chang et al., 2022) and optimal transport (Villani et al., 2009; Peyré et al., 2019; Caffarelli & McCann, 2010; Chizat et al., 2018; Chapel
'Equal contribution 'Kuaishou Inc. "Tsinghua Univer- sity SSannfor University *Jilin University, Hechang Chen <chenhe @ @chenne@@lu..cuu.... @jlu.edu.cn>.
Correspondence to:
Proceedings of the nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
2.5
Duration Buckets
60
Reconstruct
Kedium Long
40
Density
1.5
20
VAE
Watch-ratio dist.
Sampling
1.0
20
p
SUOT
Watch-ratio
60
Instances
Prototypes
(a) Watch-ratio distribution.
(b) Representation confusion.
(c) Core idca of ProWTP.
Figure 1. (a) Illustrates the watch-ratio distribution of three different video durations, demonstrating the multimodal nature. (b) Depicts the instance representation confusion problem, where MLP serves the deep recommendation model. (c) Shows the core idea of proposed ProWTP.
al., 2021), making applicable to any deep recommen- dation model. The core concept of ProWTP is illustrated in Fig.1 I()), wherein instance distributions are aligned with credible label distributions to calibrate the instance represen- tation space, thereby enhancing prediction accuracy. In the first stage, we employ a Hicrarchical Vector Quantised Vari- ational Auto-Encoder (HVQ- (HVQ-VAE) (Van Den Oord et al., 2017) to transform the one-dimensional continuous distribu- tion of watch-ratio into high-dimensional discrete distribu- tion, generating credible prototypes that effectively capture the patterns of multimodal distributions of different duration buckets. Different from traditional prototype learning (Snell et al., 2017; Yang et al., 2023; Chang et al., 2022), ProWTP generates prototype vectors from label distributions, pro- viding models with more precise and credible calibration references. Subsequently,
transform these into credible high-dimensional proto- type vectors, providing more precise reference for recommendation models calibration.
We conducted extensive offline experiments three industrial datasets and the experimental results consis- tently demonstrate the superiority of our approach.
2. Related Work
Watch-time Prediction. Watch-time prediction is a criti- cal issue in industrial recommender systems, especially for platforms focusing on short videos and movies. Despite its significance, there are only a few papers that address this area (Lin et al., 2023; Covington et al., 2016; Zhan et al., 2022). A pioneering study (Covington et al., 2016) in YouTube's video recommendation sphere introduced the Weighted Logistic Regression (WLR) technique for fore- casting watch durations. It has since been established as leading method in related application areas. Nevertheless, this approach is not directly applicable to full-screen video recommendation systems and may encounter significant bias issues due to its weighting strategy. D2Q (Zhan et al., 2022) addresses duration bias by utilizing backdoor adjustment
model the alignment between prototypes and instance representations as a Semi-relaxed Unbalanced Optimal Transport (SUOT) problem (Chapel et al., 2021), wherein the marginal constraints on the proto- types are relaxed. By reformulating the SUOT with an l2 penalty term into a weighted Lasso regression problem, we utilize regularization path algorithm compute the OT plan (Chapel al., 2021). Moreover, further enhance the model's discriminative capability, we introduce the assign- ment and compactness losses that encourage instances to cluster around their respective prototypes. Our contributions are summarized as follows:
techniques and models watch time through direct quantile regression of viewing durations. Debiased and Denoised watch time Correction (D²Co) (Zhao et al., 2023) and Coun- terfactual Watch Model (CWM) (Zhao et al., 2024) leverage causal inference frameworks, while Debiased Video Recom- mendation (DVR) (Zheng et al., 2022) employs adversarial learning to mitigate duration bias. SWAT (Yang et al., 2024) leverages a user-centric statistical framework with behavior- driven assumptions and bucketization techniques model watch time. However, those method fail to consider the ordinal relationships and dependencies between different quantiles. Additionally, since both approaches estimate watch time using point estimations, they overlook the un- certainty inherent the predictions. Then, TPM (Lin et al.,
We propose method named ProWTP for the WTP task, which addresses the instance representation fusion problem in deep recommendation models by aligning label distributions with instance representa- tion distributions through optimal transport, thereby enhancing model prediction performance.
We investigate the multimodal distribution properties of watch-ratio across different video duration buckets for the first time and utilize the hierarchy VQ-VA VA-VAE to
2

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
2023) introduced the ordinal ranks of watch time and decom- posed the problem into a series of conditional dependent classification tasks organized into tree structure.
training. The Vector Quantized Variational Autoencoder (VQ-VAE) (Van Den Oord et al., 2017) is an extension of the traditional VAE framework, which introduces a discrete latent space via a codebook of prototype vectors. In VQ- VAE, continuous latent vectors are quantized by mapping each to its closest prototype vector from the codebook, thus discretizing the latent representation. Although the quanti- zation process is dinddffferentiabl, techniques such as the Straight-Through Estimator (STE) (Yin et al., 2019) and Gumbel-Softmax (Jang et al., 2016) enable end-to-end train- ing by allowing wradient-based optimization. The prototype vectors can serve as cluster centroids (Zheng & Vedaldi, 2023; Wu & Flierl, 2020), encapsulating essential informa- tion about distinct data clusters. addition, the semantically rich prototypes learned by VQ-VAE can support various ap- plications, such as conditional image generation (Esser et al., 2021; Ramesh et al., 2022), multi-modal language model- ing (Li et al., 2023; Zhan et al., 2024) and recommender system (Liu et al., 2024; Rajput et al., 2024; Li & Sui, 2025).
Optimal Transport. Optimal Transport (OT) (Villani et al., 2009; Peyré et al., 2019) is a mathematical tool used to trans- fer or match distributions. OT has been employed in a wide range of tasks including generative adversarial training (Ar- jovsky et al., 2017), clustering (Ho al., 2017), domain adaptation (Courty et al., 2017), and others. Partial Optimal Transport (POT) (Caffarelli & McCann, 2010; Figalli, 2010) is an extension of the classical OT problem, where only partial amount of mass is transported instead of transporting all the mass between two distributions. To alleviate the com- putational load of OT, the Sinkhorn algorithm (Cuturi, 2013) was introduced as an efficient method for solving Sinkhorn OT, and it was subsequently extended to POT (Benamou et al., 2015). Previously, many methods (Flamary et al., 2016; Damodaran et al., 2018) applied OT to domain adapta- tion, aligning the distributions of source and target domains in either input or feature spaces. They utilized mini- batch OT to mitigate computational overhead but faced sampling bias since mini-batch data only partially reflect the original data distribution. To tackle these challenges, more robust OT models, such unbalanced and partial mini-batch OT, have been developed to enhance performance (Nguyen et al., 2022). Building on this, joint partial optimal transport was designed to transport only a portion of the mass, mitigat- ing negative transfer, and the method was later applied to ppen-set domain adaptation (Xu et al., 2020). Additionally, aligning source prototypes with target features has been proposed as solution to the problem of universal domain adaptation (Yang et al., 2023).
3. Background
Optimal Transport. We consider two sets of data points, denoted as {=:)==1 and {yz }== where the empirical distributions are represented as 4 Σn Mz and Pydug. respectively. Here, E 14 1 and > - U3 = 1, with 82 indicating the Dirac delta func- tion at location x. For simplicity in notation, we write / (2:,//2..... , paa) and v (v), M2;-..; Umm)" and define the cost matrix Ce R_xmm where each element is Cij - d(x;, Mj). The Optimal Transport (OT), as defined by (Villani et al., 2009; Peyré et al., 2019), is a mathemati- cal framework that transports a probability measure into another measure v with a minimum cost C. This can be formulated as the following linear programming problem:
Deep clustering with VAE. Variational Autoencoders (VAEs) (Kingma, 2013) have emerged as a pivotal approach in the domain of deep clustering for unsupervised learn- ing tasks, effectively overcoming the limitations of tradi- tional clustering methodologies that often struggle with complex and high-dimensional data. By optimizing the evidence lower bound (ELBO), VAFS facilitate the learn- ing of data embeddings while integrating prior knowledge, such as Gaussian Mixture Models (GMMs) (McLachlan et al., 2019), for modeling latent variables. Notable contri- butions in this field include the Variational Deep Embedding (VaDE) (Jiang et al., 2016) framework, which combines VAES with GMMs, employing mixtures of Gaussian priors to enhance clustering performance. GMVAE (Dilokthanakul et al., 2016) addresses the problem of over-regularization in VAE by employing the minimum information constraint. LTVAE (Li et al., 2018) improves clustering by integrat- ing latent tree model into VAE variant, introducing a tree-structured layer of discrete latent variables optimized via message passing. VAEIC (Prasad et al., 2020) jointly learns the prior and posterior parameters, thus avoiding pre-
OT( = min (T,
(I)
T)
where (..-) is the Frobenius dot product. T E R is the transport plan. I(( = (T E Rg0 BTX"|TI,,. =
μe. 1, = = denotes the polytope of matrices T.
Unbalanced Optimal Transport. However, the strict mass- conservation constraints on the transport plan T might cause dreadful degradation of performance in some applications. These constraints can be alleviated by incorporating the penalty of II(H,, v) into the objective function, which nat- urally leads to the formulation of the Unbalanced Optimal Transport (UOT) problem (Chizat et al., 2018; Chapel et al., 2021):
UOT (1,
min (T, C)+A, 4 +22 I, V): T T2
(2)
where ((.-)) is a smooth divergence measure function, T and X2 are hyperparameters that represent the strengths of
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
penalization. We also have an alternative formulation, which relaxes one of the two constraints in (I). This is a Semi- relaxed Unbalanced Optimal Transport (SUOT) problem (Chapel et al., 2021), defined as the following:
to regularize the training of the recommendation model [(: 0y) X - Rd and calibrate the instance representa- tion space, thereby producing accurate instance representa- tions h for prediction by the regressor g(:; Og) H - R".
SUOT = min Th.
(T.C) + XB v) (3)
4.2. Credible Prototypes Generation with HVQ-VAE
SUOT cast as regression. Lett -eet=vve[TT and c = vec( C). Next, we define matrices H., and H,, such that Hat com- putes the column sums of the transport plan (i.e., T 1,), and g computes the row sums (i.e., T.m). The objec- tive function for SUOT includes the transport cost (C, T) and the deviation penalty term A 1,, v), where + is typically chosen as the squared Euclidean distance. Using vectorization and matrix notation, the objective function can be rewritten as c T + H| - = 12. Introducing the variable 7= x we reformulate the problem as:
Currently, most prototype learning researches (Snell et al., 2017; Chang et al., 2022) typically rely on pre-trained mod- els, where prototypes are generated by clustering the hidden representations for subsequent tasks. However, we argue that such prototypes often contain noise and potential er- rors, limiting their capacity in calibrating original models. Therefore, we propose to generate prototypes directly from the distribution of the prediction target Y. As shown in Fig. (()), when we partition user historical behaviors into {1, ..., D} buckets based on video duration, we observe that the watch-ratio (i.e., the ratio of user's watch-time to video duration) within each bucket exhibits a distinct mul- timodal distribution. This indicates that user's behavior is statistically clustered and regular. However, these multi- modal distributions are one- one-iimenioonal long sequences, making it challenging to directly extract high-dimensional discrete representations.
1.
min yc T + 0.5 H.t 2112. s.t.H,t p. (4) t00
and as such be expressed as a non-negative penalized linear regression problem, where H.t is regressed onto the target distribution v. By representing the SUOT problem in this form, we can leverage efficient optimization algorithms from regression analysis to solve it (Chapel et al., 2021).
Pre-processing. To solve this problem, we first sample L (I D) one-dimensional distributions w = (Vi,..., yn) from each multimodal distribution. Using this sampling strategy, we transform the original one-dimensional mul- timodal distributions into D L one-dimensional near- Gaussian distributions w of length n, thereby making the data more suitable for neural networks and effectively re- ducing the difficulty of training.
4. Proposed Method: ProWTP
Let [ui,.... Upn } and = {ui, ..., flyd denote the set of users and videos, respectively, where [2| is the num- ber of users and [V| is the number of items. The user-item historical interactions are represented by D = {( vs)lz= = (u, v),, u E U, v E V where N is the number of sam- ples and y E R denotes the watch-time. The target is to learn a deep recommendation model f(X; and regres- sor (xx ))) 9g) to predict the watch-time y of user u on video v. where , and Gy is the parameters of f and g, respectively,
Credible Prototypes Generation. We observed in Fig. 1(a) that the peaks at the same positions across different dura- tion buckets exhibit similar means but varying variances. Then, we hypothesize that w sampled from the same po- sitions in these buckets can be grouped into equal means but varied variances clusters. Inspired by Vector Quan- tised Variational AutoEncode (VQ-VAE) (Van Den Oord et al., 2017), we propose a Hicrarchical VQ-VAE approach that first identifies the closest cluster and then indexes the nearest vector within that cluster. Specifically, we take the one-dimensional distribution w into the encoder E-;; Og) to obtain latent representation E (w). Subsequently, the HVQ- VAE maintains a codebook P E BC Xdd where C' and K are the number of cluster and the number of proto- type, respectively, Pij E Rd a prototype vector. We can assume that prototype vectors within the same cluster share similar means but allow for different variances.
4.1. Overview
The proposed ProWTP is two-stage method, as shown in Fig. 2. In the first stage, we employ a Hierarchical Vector Quantised Variational AutoEncoder (HVQ- VAE) P(P|Y). which consists of three components: 1) Encoder O(::O)) : Y +
-RR4 Rd maps the one-dimensional continuous watch-ratio distribution w E R2 into d-dimensional space, generating the initial representation ((w; θg) E Rdd 2) Codebook P E RC xd, quantizes the high-dimensional feature into discrete space, capturing the multimodal char- acteristics; 3) Decoder (c: Op): R - Y decodes the quantized prototype back into the continuous distribution w, ensuring the reconstruction capability of the prototypes. In the second stage, the prototypes P and Semi- Remi-Relaxed Un- balanced Optimal Transport (SUOT) modules are integrated
Next, we select the cluster e by computing the distance between E(w) and each cluster center p., where p, is ob- tained by target attention (Zhou et al., 2018; Vaswani, 2017)
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
I.
Sampling
Encoder
Decoder
Watch-ratio dist.
W
Codebook
W
Load & transformation
threshold mean
Lcompact
Modey
C
II.
X
Feature
f(X)
Instance Prototype
S
Similarity
Semi-relaxed UOT
Transport
assign
Figure 2. The framework of proposed ProWTP, which contains two phases: credible prototypes generation and distribution alignment. In the first stage, HVVQ-VÆ is used to encode the watch-ratio distribution into high-dimensional discrete representations, which serve prototypes for calibration. In the second stage, semi-relaxed unbalanced optimal transport (SUOT) is employed to align the instance distribution with the prototypes, thereby calibrating the instance space.
between ('w) and P:
4.3. distribution alignment
argmin, | EE())le, where p; softmax (Pij E(w)) Paj
As illustrated in Fig. 1(b), we posit that the inaccuracies of (5) recommendation models within the WTP task stem from instance representation confusion. This confusion hampers the model's ability to effectively differentiate between vari- ous user behavior patterns, thereby adversely affecting pre- dictive performance. To address this issue, it is imperative to utilize the generated credible prototypes P to calibrate the instance representation f(z), thereby reducing repre- sentation confusion and enhancing the model's 's predictive accuracy.
Within the selected cluster c, we find the prototype vector Pa. that has the minimum distance to E(w) and map it to the discrete vector z:
Pa,ki
where
k arg min |Pe.j E(w)ll (6)
Finally, we input z into the decoder ((-) to reconstruct In VAE, the presence of the argmin operation hampers gradient propagation. To address this issue, we employ the Straight-Throut Estimator (STE) (Bengio et al., 2013; Van Den Oord al., 2017) during training, with the loss function defined as follows:
Transport Matrix Calculation. First, conceptualize the instance representations f(r) and the prototypes P as two probability distributions, with the objective of mapping the instance representation distribution a 1,, to the prototype representation distribution 3 dklck through optimal transport (Villani et al., 2009; Peyré et al., 2019; Chapel et al., 2021). Specifically, we construct the instance representation set H = {h,, h2...., hn, C Rd, where each instance representation h, is obtained by L2 normaliza- tion of the model output f(x,), i.e. h, = f(xx)/1l (f and n& the mini-bated size. The prototype [Pi, , P2....; PCx) R&d is derived from the original pro- totype set P through a learnable linear transformation W. To quantify the discrepancy between instances and proto- types, we define the cost matrix C E RCC where each element Cr, represents the cosine distance hetween instance h; and prototype pa. i.c. C H²
CHVO-VAR = 1/w ((EE(w) + sglz EEw))
+ +[[sg[E(w)]
2 E(w) sg[z]]
P
Here, sgl-] denotes the stop-gradient operation, which halts gradient flow during backpropagation, and / is hyperpa- rameter that balances the reconstruction loss and the embed- ding update. Through this approach, we transform the one- dimensional continuous w distribution into discrete high- dimensional prototype vectors, thereby providing credible calibration for subsequent models.
5

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
To achieve distribution alignment, we adopt the optimal transport method. However, traditional optimal transport re- quires all the mass from 3 is transported to a, meaning that each prototype must be fully mapped to the instances. This strict marginal constraint is not applicable in our scenario, especially in a mini-batch setting, where it is unreasonable to allocate samples for every prototype, as certain prototypes may not correspond to any instances in the current batch. Therefore, we model the alignment between instances and prototypes as Semi-relaxed Unbalanced Optimal Transport (SUOT) problem (Chapel et al., 2021):
that these samples should be close each other in the in- stance representation space. Inspired by contrastive learning (Khosla et al., 2020), we designed a compact loss to en- courage samples under the same prototype to cluster more closely in the representation space:
CK Rs
1
Ceompact
Σ
CK
R=
exp(h,
(11)
log
exp(hThy//T)
min T = (a, T20.Tlckaa
(T,C)+|| In -112
where I(-) is the indicator function, and T controls the smoothness. By minimizing the compact loss, we not only help reduce instance representation confusion but also en- hance the model's ability to capture fineggrained features, ultimately improving prediction performance. Additionally, to address computational efficiency issues arising from mul- tiple loops, we randomly sample 20% of the instances from the mini-batch for the calculations. Finally, We incorporate the labels y; to define the MSE loss:
(8)
where À controls the strengths of penalization. By intro- ducing an 12 penalty term into the objective, we allow the marginal constraints on the prototype side to be relaxed, transforming the hard constraints T 33 into soft one. To optimize this problem. (Chapel et al., 2021) reformu- lated as a weighted Lasso regression and solved with a regularization path algorithm.
Training objectives. To calibrate the sample space, we aim for the instance representations to cluster tightly around their corresponding prototypes, necessitating a reduction in the distance between each sample and its assigned proto- type. Each row of the transportation matrix T represents the allocation relationship of sample x; to the prototypes, with the row sums equal to C.K- After multiplying by the constant C' x K, each row of T can be viewed as soft pseudo-label summing to 1. Therefore, we can define the calibration loss through the cross-entropy loss:
N
CK
Liaok
(9)
t.APk) - v.)3
(12)
N
Compared to original prediction, ProWTP reshapes instance representations f(r)) in the credible prototype space P by utilizing the transport matrix T to weight and combine pro- totype vectors, subsequently feeding these representations into the regressor g(: 6,) for prediction. This approach effectively captures the inherent structure of the instance representation space, enhancing the model's robustness and leading to more accurate predictions.
1 Thb
mi CoK
exp (h & PH/T)
Lassign
lip log C.CK
Li=1 exp (h * P//T)
(9) where T is the temperature parameter that controls the smoothness of the softmax function. By minimizing Lassign, we can decrease the distance between samples and their corresponding prototypes, thereby better calibrating instance representations within the prototype space, reduc- ing representation confusion, and enhancing the model's 's predictive performance.
5. Experiment
5.1. Setup
Dataset. We adopt two public datasets Wechat (collected from Wechat App) and Kuairand (Gao et al., 2022) (from Kuaishou App). one private dataset Short-video (from our App) for offline experiments. We split each dataset into training, validation and test set by the ratio of 6:2:2.
To further shape the instance space, we hope for instances assigned to the same prototype to be closer together in the representation space, thereby forming tighter clusters. This necessitates promoting similarity among samples under the same prototype. To achieve this, we first define the set of instances associated with each prototype k:
Baselines. We evaluate the performance of proposed ProWTP in comparison with the following baselines that represent the popular method in WTP tasks: Traditional Re- gression. Weighted Logistic Regression (WLR) (Covington et al., 2016), Ordinal Regression (OR) (Crammer & Singer, 2001),
Quantile (D2Q) (Zhan et al., 2022), Tree-based Progressive Model (TPM) (Lin et al., 2023). Sine all meth ods are model-agnostic, Debiased Video Recommendation (DVR) (Zheng et al., 2022), and Counterfactual Watch Model (CWM) (Zhao et al., 2024), we implement them on the MLP (Taud & Mas, 2018).
1 = Lilte,
VEh Σ t
(10)
which includes those samples under prototype Pk whose transport value li, exceed the average level, indicating
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Calibrating Video Watch-timc Predictions with Credible Prototype Alignment
Table 1. Overall performance of different methods. Roldface means the hest-performed methods. Higher XAUC and XGAUC indicate better performance, while lower MAE and RMSE are better.
Model
Metrics
TR
WLR
OR
D2Q
TPM
DVR
CWM
ProWTP
RMSE
30.39
30.24
28.96
29.12
28.85
28.91
28.78
28.47
MAE
20.53
20.16
20.05
20.12
19.97
20.05
19.90
19.84
WeChat
XAUC
0.5985
0.6047
0.6078
0.6094
0.6107
0.6109
0.6115
0.6183
XGAUC
0.5409
0.5538
0.5575
0.5616
0.5645
0.5628
0.5654
0.5730
RMSE
42.41
42.17
41.44
41.65
40.82
40.97
40.75
40.45
MAE
28.09
27.98
27.69
27.82
24.58
26.08
24.54
24.43
KuaiRand-Pure
XAUC
0.7176
0.7081
0.7145
0.7189
0.7203
0.7201
0.7209
0.7290
XGAUC
0.6907
0.6885
0.6945
0.6990
0.7024
0.6998
0.7026
0.7048
RMSE
30.59
30.22
29.18
29.35
29.02
29.18
29.00
28.67
MAE
11.46
11.29
11.07
11.15
10.82
11.08
10.76
10.69
Short-video
XAUC
0.5744
0.5788
0.5705
0.5814
0.5831
0.5822
0.5848
0.5929
XGAUC
0.5537
0.5603
0.5609
0.5622
0.5667
0.5643
0.5681
0.5731
Evaluation. To evaluate the performance of each model, we use four widely adopted metrics (Zhan et al., 2022): MAE, RMSE, XAUC, and XGAUC.
Impact of different modules in ProWTP. We further con- duet ablation studies to demonstrate the effectiveness of the key components of ProWTP and the results are shown in Tab. 2. Specifically, we compare ProWTP to its five variants: (1) w/o HVQ-VAE, means that prototypes are no longer generated from label distributions but are randomly initialized as parameters within the neural network. (2) w/o Lassign means the assign loss is removed.
Training details. We set the embedding dimension of all features to 64. For TR and OR, models are implemented on MLP with three hidden layers and ReLU (Glorot et al., 2011) as the activation function. For other baseline methods, we adopt the experimental design and parameter settings de- scribed in the original papers. We optimize all models using Adam optimizer (Kingma& & Ba, 2014) with the batch size of 512 on both two datasets. To avoid overfitting, We set the dropout rate (Srivastava et al., 2014) to 0.2 and employ an early stopping mechanism (Prechelt, 2002) with a patience of 10 epochs. Among them, the learning rate is searched in (le-3, le-4, 1e-5). and 3 is tuned from 0.0 to 0.2 with increments of 0.05. K is searched in {4, 8, 12, 16, 20, 24).
(3) w/o Ceompact means the compact loss is further removed. (4) w/o SUOT indicates that SUOT is longer used for dis- tribution alignment, and instead, the the linear combination of prototypes is directly computed for prediction. (5) w/o WTP means the approach degenerates into traditional re- gression (TR). The results indicate that removing any single module leads to a performance decline, demonstrating that each component of ProWTP is crucial for improving model performance. Removing HVQ-VAE results in significant drop in performance, highlighting that transforming label distributions into credible prototypes effectively enhances the model's 's performance. The impact of removing SUOT is also particularly notable, indicating that SUOT helps better align the distributions of instances and prototypes, thereby improving predictive capabilities. Moreover, the two loss functions effectively constrain the learning of the instance space, ensuring instances are tightly clustered around the corresponding prototype, which enhances the model's dis- criminative ability.
5.2. Results
Comparison with baselines. We compare ProWTP with several baseline methods on two real- world industrial graaee datasets, and the results are shown in Tab. 1. ProWTP achieves the best performance across all evaluation met- rics. In contrast, the TR method performs the worst on both datasets, likely because it directly regresses on watch-time without leveraging the distributional characteristics of the data. WLR and OR show some improvement over TR, but the gains are limited. The D2Q, by addressing duration bias, improves prediction accuracy, and TPM further en- hances performance through its strusttret modeling of dependencies and uncertainties. ProWTP outperforms all basclines in four metrics, particularly with significant improvements in RMSE and XAUC. This demonstrates that ProWTP effectively alleviates instance representation con- fusion by aligning the credible prototype distribution with the instance distribution, improving model's accuracy.
Different prototype generation methods. To further val- idate the effectiveness of using HVQ- VAE for generating credible prototypes, we compare three different generation strategies: VQ-VAE, Kmeans, and Random. As shown in Tab. 3, the performance of VQ-VAE saw slight de- crease, indicating that the hierarchically generated proto- types from HVQ-VAE exhibit better clustering structure, making it easier for instance representations to align with them. Kmeans generates prototypes by clustering the in-
7

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Table 2. Ablation results of different modules in ProWTP.
Wechat
KuaiRand-
Model
RMSE
MAF
XAUC
XGAUC
RMSE
MAE
XAUC
XGAUC
ProWTP
28,47
19.84
0.6183
0.5730
40.45
24.43
0.7290
0.7048
ProWTP w/o HVQ-VAE
29.12
20.23
0.6128
0.5690
41.08
24.87
0.7233
0,7010
WTO w/o Lassign
29.45
20.65
0.6112
0.5678
41.35
25.01
0.7205
0.6998
ProWTP w/o Lcompact
29.38
20.51
0.6130
0.5684
41.12
24.92
0.7221
0.7004
ProWTP w/o SUOT
29.90
20.89
0.6108
0.5665
42.00
25.50
0.7185
0.6980
ProWTP
30.39
20.51
0.5979
0.5406
42.41
28.09
0.7174
0.6905
Table 3. Ablation study different prototype generation methods.
Prototypes
Wechat
KuaiRand- Pure
generation
RMSE
MAE
XAUC
XGAUC
RMSE
MAE
XAUC
XGAUC
HVQ-VAE
28.47
19.84
0.6183
0.5730
40.45
24.43
0.7290
0.7048
VQ- VAE
28.82
20.04
0.6164
0.5713
40.72
24.52
0.7259
0.7024
Kmeans
29.07
20.28
0.6132
0.5683
41.22
24.98
0.7236
0.7018
Random
29.12
20.23
0.6128
0.5690
41.08
24.87
0.7233
0,7010
Table 4. Ablation study on different distribution alignment methods.
Distribution alignment
Wechat
KuaiRand-
RMSE
XAUC
XGAUC
RMSE
MAE
XAUC
XGAUC
SUOT
28.47
19.84
0.6183
0,5730
40.45
24.43
0.7290
0.7048
OT
28.82
20.15
0.6164
0.5705
40.85
24.65
0.7252
0.7023
UOT
29.46
20.58
0.6137
0,5688
41.20
24.93
0.7225
0.7000
w/o alignment
29.90
20.89
0.6108
0.5665
42.00
25.50
0.7185
0.6980
stance representations directly, but its performance drops due to being more susceptible to noise and potential errors. The Random method performs the worst, as it fails to pro- vide a credible reference for calibrations, thereby affecting the model's predictive performance.
Impact of the number of prototypes K. In Fig 3, illustrate the impact of varying the number of prototypes
K on model performance. As the number of prototypes increases, performance improves accordingly. However, defining too many prototypes results in slight performance fluctuations, likely due the introduction of noise.
Different distribution alignment methods. We also com- pare different alignment strategies, shown in Tab 4. The
SUOT approach, which relaxes the marginal constraints the prototype side, yielded the best performance. In con- trast, OT requires strict transportation of the entire mass, but since not all prototypes in a mini-batch can be assigned to in- stances, it limits performance. UOT also saw a performance decline due some instances not being assigned. SUOT's flexible allocation mechanism more effectively enhances model performance.
6. Conclusion
In this paper, we propose two-stage method, ProWTP, for watch-time prediction (WTP) tasks, applicable to any deep recommendation model. This method aligns label distri- butions with instance representation distributions through prototype learning and optimal transport to calibrate the in- stance space, thereby improving the accuracy. Specifically, employ HVQ-VAE to transform continuous watch-ratio labels into high-dimensional discrete distributions, which serve as credible prototypes. Then, the alignment between prototypes and instance representations is modeled as a SUOT problem, where the marginal constraints are relaxed and the problem is reformulated weighted Lasso regres- sion for solution. Additionally, introduce assign loss and compact loss encourage instances cluster tightly around their respective prototypes. Finally, extensive exper- iments demonstrate the significant advantages of ProWTP in practical applications.
0.62
0.
28
RASE IMAE
0.60
0.61
/XGAUC
RASS TAKE
35
AUSE MEE
0.72
XXGMIC
XAUC
MAUC
0.39
22
0.38
20
25
Prokotypes
Numberol Protolypes
(a) Wechat
(b) KuaiRand-Pun
Figure 3. Impact of the number of prototypes K on two datasets.
8

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Impact Statement
nalized linear regression. Advances in Neural Information Processing Systems, 34:23270-23282, 2021.
This work introduces ProWTP, calibrated watch-time pre- diction framework that aligns model representations with credible label-derived prototypes using semi-relaxed unbal- anced optimal transport. It brings clear practical benefits to industrial video recommendation systems by improv- ing prediction accuracy, reducing representation confusion, and better modeling the multimodal nature of user behav- ior. These improvements can enhance user experience, con- tent exposure fairness, and platform efficiency. However, like all engagement optimization techniques, ProWTP may raise concerns about potential over- personalization rein- forcement of addictive behaviors. Nonetheless, since the method focuses on improving calibration fidelity rather than increasing engagement directly, we believe its responsible deployment can lead to more transparent and equitable rec- ommendation systems.
Chizat, L.., Peyré, G., Schmitzer, B., and Vialard, F.-X. Scal- ing algorithms for unbalanced optimal transport problems. Mathematics of Computation, 87(314):2563-22 2609, 2018.
Courty, N., Flamary, R., Habrard, A., and Rakotomamoniy, A. Joint distribution optimal transportation for domain adaptation. Advances in neural information processing systems, 30, 2017.
Covington, P., Adams, J., and Sargin, E. Deep neural net- works for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems, pp. 191-198, 2016.
Crammer, K. and Singer, Y. Pranking with ranking. Ad- vances in neural information processing systems, 14, 2001.
Acknowledgments
Cuturi, M. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26, 2013.
This work is supported in part by the National Natu- ral Science Foundation of China (No. 624761 10, No. (22311299;; the NSF under grants 111-1066588 and POSE- 2346158; the National Key R&D Program of China (No.
Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., and Courty, N. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In Proceedings of the European conference on computer vision (ECCV), pp. 447-463, 2018.
2021ZD01 12500): the Key R&D Project of Jilin Province (No. 20240304200SF); and the International Cooperation Project of Jilin Province (No. 20220402009GH).
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
A. Appendix
A.I. explanations on instance representation confusion.
A.1.1. MATHEMATICAL EXPLANATION
Proposition A.1. WTP task, let the instance representation of a sample (z,y) be f(z), with its ideal center being By = [x) v), where y is the ground-truth. The degree of instance representation confusion is defined as the distance between the instance representation and the ideal center, d(f(x), Mby) = llf(z) - Pyll- Then, the model's prediction error
A, y is predominantly correlated with the degree of instance representation confusion d( (x),
Proof:
In regression model for WTP task, suppose the predicted value is given by ÿ f(x) and the true value y is function represented by ideal center My (z) yl and noise term e:
ReLU(( By + b) +
(13)
where W E R2xd and 6 IR are the model parameters, and f(x) E R is the instance representation of the input æ. The noise is independent and identically distributed Gaussian noise with zero mean, unrelated to the instance representation, i.e, 6 ~ o2)
Starting with the model and true value definitions, the error can be rewritten as:
A y أ -ReeLU(WH4, My + b) + c ((z) + b]]-
(14)
We assume that the ideal center My lies within the activation region, meaning that WHy + +b 2 This assumption holds because, in WTP task, the ground-truth y 2 Thus, we only need to consider two cases based on the value of W f(x) + b for each sample:
(1) Case 1: fxxx) +
In the linear activation region of ReLU, the output simplifies to:
A₂ (W My + b b b) (W](x) + b)
(15)
Further simplifying:
A = =W f(x)) tel.
(16)
The squared error is:
A (W f(x))) 2cW - f(x)) + 2.
(17)
Taking the expectation, assuming is independent of fz) and E[e] 0:
E[A ( (14 f(x))) B
(18)
Since Eie?] o², we have:
E A (Wyy-f(x))) f(x)))² +
(19)
Thus, the expectation of the squared error is dominated by (W {()))) and we get:
(W (pg f(x))) || [lf(x) 1l
(20)
Therefore:
E[A x [lf(z) 12,112
(21)
(2) Case (f(x++++++ (1)))bb<<
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
In the non-activation region of ReLU, ((x) b 0, then:
O.
(22)
In this case, the error is:
A = lv/-
(23)
Combining both cases, the expected squared error is:
B = P(W f(x) + + 0) (IW1)2 (1f(x)) + o²) + P(W f(z) +b< y².
(24)
When most instances satisfy W fz)) + > 0 (i.e., the ReLU activation region dominates), the expected error is primarily determined by (x:) pyll- We conducted experiments and found that instances located in the non-activation region of ReLU account for approximately 1% to 2% of the total training data.
A.1.2. DIFFERENT MODEL. ANALYSIS
In this paper, we identify instance representation confusion as the main reason for the inability of existing methods to achieve accurate predictions. In Appendix 1. we provide a mathematical explanation of the phenomenon. In this section, we conduct a visualization study on the relationship between instance representations fxx and prediction errors A across different values.
To simplify the analysis, we focus three sample groups with true values y (0,0.1), y E [1.0, and y [2.0, 2.1). For each sample x, the prediction error of the model f(-) is denoted A. We define the ideal center Bhy as the average instance representation fxx) of samples with A < 0.01. The degree of instance representation confusion is measured by the L2 distance [I](x)) - Myll-
The analysis results for each model include five figures: (a) The correlation between prediction error and the degree of confusion. (b) t-SNE visualization of instance representations ff (x) for all three sample groups with (A < 0.3). (c)(d)() The visualization of instance representations [xx) and ideal centers uy for high-error samples (A 0.3) in y E [0, 0.1),
y E [1.0, 1.1) and y E [2.0, 2.1) respectively.
From Figure (a), it can be observed that both TR and Pro align with the conclusion of Appendix 1.1.1, where the prediction error A is positively correlated with the degree of confusion. From the distribution of black scatter points, TR exhibits a significantly higher level of confusion, while PTP effectively mitigates this confusion by reducing the distance between instances and reliable prototypes.
From Figure (b), even when the prediction error A 0.3, the instance representations of TR struggle form well-defined clusters, with instances of different types mixed together. In contrast, ProWTP achieves clear clustering among instances with small errors, and instances of different types are distinctly separated.
In Figures (c), (d), and (e), for points with larger errors, darker colors indicate higher A values and greater distances from the ideal center. This further supports the conclusion in Appendix 1. Additionally, compared to TR, ProWTP shows significantly fewer points with large errors (i.e., fewer dark-colored points), effectively reducing instance representation confusion. This demonstrates that the root cause of reducing prediction errors lies in learning better instance representations.
A.2. More details of Pre-processing and Prototype generations.
Our goal to transform the ground-truth Y of the entire dataset into high-dimensional vectors, referred to as prototypes, for downstream tasks. The generation process is divided into four steps, as shown in Fig. 6:
1. Partitioning Y.
The ground-truth Y is an N x vector, where N is the size of the dataset. Directly generating prototypes from this vector is challenging. We observe that watch-ratio in different duration buckets exhibit distinct multi-modal distributions. Thus, Y is first divided into 1 unequal-length multi-modal distributions based on video durations.
2. Fitting Gaussian Mixture Models (GMMs). Even after partitioning, the watch-ratio distributions remain as long dinedimnsioonal continuous arrays, making direct modeling still difficult. To address this, we fit D GMMs to these distributions, where the number of components C' corresponds to the number of peaks in the distribution.
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
à 0.3
Center (4 x0.01)
25
2.0
05
00
Denety
Scatter with marginal distributions
(b) Three different instances
=0000
&
1.5
1.0
0.5
0.0
0.0
0.0
Center
(c) Instance with y 0.1)
(d) Instance with y [1, 1.1)
(c) Instance with [2, 2.1)
Figure Traditional Regression (TR) on Wechat.
5.3 Genter (& 0.01)
Danaky
Scatter with marginal distributions
(b) Three different instances with A < 0.3
- = 0.01
0.01
A=
Center
(c) Instance with y [0, 0.1)
(d) Instance with y [1, 1.1)
(e) Instance with 2.1)
Figure ProWTP on Wechat.
14

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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
8 (4 - 11
GMM,
HVQ- AAn
N+
0.1,
2.2,
Divide
short
Fit
Training
VAE
Based on
1.8,
Duration
GMM
long
Bj
(
oo +I+
Ground-truth
Watch-ratio dist.
One-dimensional dist.
Prototypes generation
Figure 6. The details of Pre-processing and Prototype generations.
3. Random Sampling. For each GMM, C sets of means, variances and weights (()){=, are obtained. The sampling process as follows:
Data for each peak sampled randomly to form a distribution w = (y11 .......... yp) of length n, with the sampling range defined as ju o, μ + i o). where i follows Gaussian distribution N(u ,
To ensure that the overall sampled distribution matches the original distribution, the number of samples for each peak is determined by the weights 9. Specifically, when generating L near- Gaar-Ga distributions from the current modall distribution, the allocation of is governed by 0, where the number of samples generated for each peak is [0 L.J.
This sampling strategy significantly simplifies subsequent learning while adhering to the semantic of Prototypes, where each Prototype represents the center of peak.
4. Generating Credible Prototypes. For each bucket, L distributions are sampled, resulting in D x L distributions. These are fed into the HVQ-VAE for training, and the codebook weights from HVQ- VAE are considered as Prototypes.
In Fig. and 8, we present a comparison the overall distribution of 100 sampled w values (orange) and the original watch-ratio distribution (blue) across different durations on the WeChat and our Short-video datasets, respectively. The red dashed lines indicate the means of the GMM. It be observed that the distributions exhibit typical multimodal characteristics, and the sampled distributions successfully preserve the original distribution's 's shape and features.
Original dist. Sampled dist. Mean
Original dist. Sampled dist. Mean
Original dist. Sampled dist. Mean
1.75
1.50
Density
1.25
2.5
0.2
0.25
0.0
0.00
0.0
Watch-ratio
Watch-ratio
Watch-ratio
(a) Short.
(b) Medium.
(c) Long.
Figure 7. Comparison of Sampled Distribution and Original Watch-Ratio Distribution on WeChat.
A.3. The Relationship Between Multimodal Distributions, Prototypes, and User Behavior.
The watch-ratio distribution exhibits distinct multi-modal characteristics, reflecting different user behavior patterns during video consumption: "scroll" (the first peak) indicates that users skim past the video after watching the cover for about I second, showing lack of interest; "like" (the second peak) represents users who watch most the video and show moderate interest; and "very like" (subsequent peaks) suggests users who are highly engaged with the content and may even
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
2.00
Original dist. Sampled dist.
Original dist.
Original dist. Sampled dist.
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uensry
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0.0
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2.0
Watch-ratio
Watch-ratio
Watch-ratio
(a) Short.
(b) Medium.
(c) Long.
Figure 8. Comparison of Sampled Distrihution and Original Watch-Ratio Distribution Short-video.
re- watch multiple times. This clustering behavior helps watch-time prediction models quickly identify specific intervals, thereby reducing prediction errors.
However, multi-modal distributions typically long one-dimensional sequences, making direct modeling challenging for capturing behavior patterns effectively. Prototype learning addresses this issue by dividing the multi-modal distribution into several sub-distributions and generating multiple semantic centers in high-dimensional space for each sub- hob-distribution. This approach significantly simplifies computation and learning complexity, breaking down the complex multi-modal distribution into more manageable local structures. Consequently, Prototype enables watch-time prediction models to more accurately capture the characteristics of different user behavior patterns, improving global prediction performance of duration distributions and effectively supporting recommendation systems in WTP tasks.
The face doesin't attract me, so I scroll past.
At seems good, so I want finish it.
scroll
Really love this! Watching it again and again!
FUR MITHONN
AA
MURR. EERAA
3
again finish
scroll
like
very like
Figure The Relationship Between Multimodal Distributions, Prototypes, and User Behavior.
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
A.4. Computational Complexity Discussion
Stage I: prototype generations. The computational complexity of HV primarily arises from the cluster selection and prototype selection. For single sample, the cluster selection involves computing the attention- attention-weightel weighted cluster centers, with time complexity of O(C K + C), where C' is the number of clusters, K is the number of prototypes per cluster, Within the selected cluster, prototype selection further incurs a complexity of (K)). Overall, the time complexity for single sample is OCC K + C + K). and the space complexity is dominated by the static storage of the codebook, which is O(C' K d), and d is the prototype vector dimension.
Importantly, HVQ- VAE is completely independent, and its spatio-temporal complexity does not affect the training and inference time of ProWTP. When the distribution of watch ratios is sufficiently large, the resulting prototype distribution is stable. Furthermore, we observed that for well-established video recommendation APP, the watch-ratio distribution remains largely unchanged and consistent across multiple months.
As shown in Figure 10, we randomly sampled 200,000 users from our APP (a short-video platform) and extracted their historical behavior on the 1st day of each month from January to November 2024. The data were divided into D = 15 buckets based on video duration. We then computed the Wasserstein Distance between the watch-ratio probability density distributions of each month and November, as well as the Kolmogorov-Smimmo test with p < 0.05 between their cumulative empirical distributions. The results indicated significant distribution shifts across multiple months.
Even in extreme scenarios where user behavior undergoes notable adjustments, we only need to resample the watch-ratio distributions for each duration buckets, perform offline retraining. and update the weights of PTT This process incurs minimal computational overhead.
0,50
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Duration Bucket
(a) Wasserstein Distance.
(b) Kolmogorov- Smiinov test with p 0.05
Figure 10. Mean Differences in Watch-ratio Distributions Between November and January-October
Stage II Distribution aligument. ProWTP is a model-agnostic method that adds only an additional prototype layer compared to the baseline, resulting in a space complexity of OC During the inference phase, the OT module is removed, and the final value is computed as linear combination of similarities to each prototype, which is then input into the regressor. The time complexity of this process OCC), where C' and K are small constants, ensuring that the time overhead remains negligible. The training time complexity comes from four parts. OT optimization operates on transportation matrix of size n& x CK, where ny is the mini-batch size and CK is the number of prototypes, with complexity of O0I-m,,CC ) for 7 iterations. The calibration loss, which computes softmax and cross-entropy for each sample across all prototypes, has a complexity of O(n,- CK). The compact loss, which encourages tighter clustering of instance representations under the same prototype, involves sampling 20% of the instances and computing pairwise similarities, with a complexity of (0.04 CK 1st 12). Additionally, the prototype-weighee O(n, CK). Thus, the overall training time complexity for a batch is O n4 CK + n4 CK + 0.04 CK -KS| 18)
prediction calculation incurs an additional
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
We trained on WeChat data with batch size of 512 using an RTX 4090 GPU. Tab. 5 compares the training time per batch for PTP under different sampling frequencies and the corresponding changes in RMSE, along with the inference efficiency of different models. It can be observed that ProWTP's inference efficiency does not significantly increase compared to the baseline. However, as the sampling ratio increases, the training time for ProWTP grows noticeably, while the performance improvement shows diminishing marginal returns.
Table Time cost (s) per batch of different models on Wechat.
Model
TR
D2Q
PTP
Sample ratio
0%
10%
20%
30%
50%
100%
Train cost
0.011
0.013
0.049
0.058
0.061
0.075
0.092
0.121
RMSE
30.39
29.12
29.38
28.91
28.47
28.22
28.05
28.04
Infer cost
0.003
0.003
0.004
A.5. Datasets.
Wechat: This dataset was adopted in WeChat Big Data Challenge', which records the behavior of users on short videos in two weeks. We divide the duration into D buckets. The user_id, device_id, video author id, duration_level and multi-model content feature vectors are used as feature inputs.
2) Kuairand-Pure Constructed from the recommendation logs of the video-sharing mobile app, Kuaishou (Gao et al., 2022), the dataset contains millions of intervened interactions about 27,285 users and 7,551 items in 4 weeks. Similarly, we discretize the duration into D 5 buckets in this dataset, and the user_id, video_id, tab, music_id, author_id, deration_level and wwl.acttvveeegrree will serve as input features in our experiments.
3) Short-video: We collected behavioral logs of 200,000 active users from short-video platform on November 2024. The data was divided into D 15 buckets based video duration. In our experiments, used the following features as inputs: user_id, video_id, lag_id, author_id, and devetion_level.
Table 6. Statistical Information of datasets.
Data
#user
#video
#interaction
#duration
WeChat
20,000
96,428
7,210,290
Siairand-Pure
27,285
7,551
1,231,181
5
Short-video
200,000
4,832,885
30,000,000
15
A.6. Baseline Details.
To evaluate the effectiveness of our proposed method, we compare it with the following methods that are pivotal in leveraging Watch-time prediction:
TR (Traditional Regression): This method adopts a straightforward regression approach, using watch time as the label. It is trained to minimize the Mean Squared Error (MSE).
WLR (Weighted Logistic Regression) (Covington al., 2016): As implemented in YouTube's 's system, this method learns a logistic regression model, reweighted by watch times, and uses the learned odds to estimate watch time during prediction.
OR (Ordinal Regression) (Crammer & Singer, 2001): This method, based on ordinal regression techniques, empha- sizes the relative order of watch times, fitting the data to predict categorical watch time levels.
D2Q Dhuration--eccoffuunded Quantile) (Zhan al., 2022): Representing state-of-the-art approach in watch time
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
prediction, this model addresses duration bias through backdoor adjustment and fits duratiion-dependent quantiles of watch time using MSE.
TPM (Tree-based Progressive Model) (Lin et al., 2023): This approach uses tree-structured series of classification tasks, considering ordinal ranks and prediction variance, and incorporates backdoor adjustment to mitigate bias, offering nuanced and comprehensive approach to enhancing watch time prediction in video recommender systems.
DVR (Debiased Video Recommendation) (Zheng et al., 2022): This methods provides unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning.
CWM (Counterfactual Watch Model) (Zhao et al., 2024): This methods proposes to use counterfactual reasoning to mitigate duration bias.
A.7. Metries.
Root Mean Square Error (RMSE). This metric measures the average magnitude of errors between generated values and actual values, which formulated as:
RMSE
Σ f - 0:)3
(25)
where yi is the actual value of the i-th sample and d is the predicted value.
Mean Absolute Error (MAE). This metric used evaluate the average discrepancy between generated and real data; the calculation is follows:
1 n 71
MAE
Σ -9ll-
(26)
XAUC (Zhan et al., 2022). This is an extension of the standard AUC, applied to continuous values. Given pair of samples (i,j), if the predicted watch-time values ÿ and új are in the same order their true values yE and y/, the score is otherwise, the score is O. We uniformly sample such pairs from the test set, and the XAUC is computed the average score over all pairs. The formal definition is:
XAUC
1 S
Σ (1,))S
Il( > 8,) = (y: > v,)l,
(27)
where S represents the set of all sampled pairs, and I(-) is the indicator function, which returns I if the predicted order matches the true order, and 0 otherwise. XAUC intuitively measures how well the ranking induced by the predicted watch times aligns with the true ranking. higher XAUC indicates better model performance.
XGAUC (Zhan al., 2022). This is weighted version of XAUC. It computes XAUC for each user individually, and then averages the XAUC values with weights proportional to the sample size of each user. The formal definition is:
XGAUC En No XAUC. Σ. Nu.
(28)
where u represents a user, N₂ is the number of samples for user u, XAUC,, is the XAUC score for user u. XGAUC measures the overall ranking consistency across users, with the weight adjusted based on the number of samples per user. A higher XGAUC indicates better model performance across users.
In WTP tasks, MAE and RMSE are used to measure how close the predicted watch times are the actual values, focusing on the accuracy of the predictions. XAUC and XGAUC, on the other hand, evaluate how well the predicted rankings of watch times match the true rankings, emphasizing the importance of the order of predictions. Both metrics are crucial: accurate predictions (measured by MAE and RMSE) ensure precision, while correct rankings (measured by XAUC and XGAUC) are essential for delivering relevant recommendations. In recommendation systems, maintaining the correct ranking is often as important, if not more so, than predicting the exact values, making both aspects vital for optimizing user satisfaction and overall model performance.
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
A.8. The derivation of -AVQ-VAE-
The loss function LиvY-VAE designed optimize both the encoder and decoder networks, while preserving the discrete nature of the latent space.
Luvo-vAE = 1|w - ((E(w) + sg|z - [(()))
(29)
+ [|g[[[(]] - =|12 = s
This function consists of three key components, explained as follows: Reconstruction loss:
- ((E(w) + sg(z F(w)))lB
(30)
This part measures the squared Euclidean distance between the decoder output D(-) and the original input w, assessing the model's ss ability to reconstruct the data. Here, E(w) represents the encoder output of the input w, and z is the nearest prototype vector. The stop-gradient operation sg[-] prevents gradients from passing through, ensuring that the codebook is only updated through the second term. During forward propagation (when calculating the loss), this simplifies to D(E(w) + E(w)) Dzz), and during backpropagation (when calculating the gradients), since E(w) provides no gradients, it also simplifies to ((E()))
Quantization Loss:
[sg[E((w) -
(31)
This loss encourages the prototype vector z to move closer the encoder output E(w). The stop-gradient operation is applied to F(w) to prevent gradients from propagating through this term the encoder, thus only updating the codebook.
Commitment Loss:
B| ((w) - sg[z]113
(32)
This term encourages the encoder output F(w) commit the chosen codebook vector The weight factor adjusts the importance of this loss relative the other components. By increasing the encoder's commitment its quantized representation, this term improves the model's stability and efficiency.
A.9. Results different duration buckets.
A.10. Results on different duration buckets.
Table Results on different duration buckets.
Duration bucket
Wechat
KuaiRand-Per Pure
RMSE
MAE
XAUC
XGAUC
RMSE
MAE
XAUC
XGAUC
0
8.57
6.87
0.61 18
0.5273
7.82
5.57
0.6922
0.6365
13.13
10.53
0.6084
0.5334
16.40
12.37
0.6689
0.6085
20.15
16.19
0.6088
0.5289
28.79
21.45
0.6941
0.6245
31.95
26.00
0.5930
0.5261
43.62
32.03
0.6786
0.6167
48.97
40.34
0.5795
0.5203
69.09
48.40
0.6614
0.6153
A.11. Training Loss.
L Lrask + Lassign + B Leompact;
(33)
where 3 is the pppemmearreete ranged from (0.0, 0.2].
A.12. Why OT?
Assuming the instance representation is h, and the prototype set is [px)C=k K the weight between h; and each prototype pa defined as:
exp (h pp/TT)
0'i Σ j=1 exp (h P//T)
(34)
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
We consider the three different alignment methods:
SUOT calculates a transport matrix based on the relationship between prototypes and instances, and uses i E to guide the learning of a. This approach considers global distribution alignment, offering strong robustness and interpretability.:
116
C+K
Lassign
4i log 0i,
(35)
1hh
L2 distance directly aligns representations, focusing on point-wise alignment without considering the global distribution. This makes it susceptible to the influence of outliers.:
C+K Σ
Lassign
|lb;
P
(36)
w/o alignment directly uses the linear combination Σetk a * P& for prediction without Lassign.
Tab. and 8 compare the results of different alignment methods, showing that SUOT achieves the best performance, which demonstrates the effectiveness of OT- based alignment. Fig. 11 provides a case study where we visualize the weight matrix of batch (ni, = 512, C' * K 80) from WeChat dataset. It can be observed that the a learned by OT alignment maintains the same sparsity as the transport matrix T. In contrast, the from other methods very dense, treating the prototypes mere representation anchors to enhance the overall representation, while ignoring whether instances should actually match their corresponding prototypes.
Table 8. Different distribution alignment methods.
Distribution alignment
Wechat
KuaiRand-Puri
RMSE
XAUC
XGAUC
RMSE
MAE
XAUC
XGAUC
SUOT
28.47
19.84
0.6180
0,5727
40.44
24.33
0.7288
0.7045
L. distance
20.35
0.6129
0.5683
41.28
24.91
0.7208
0.7006
w/o alignment
29.90
20.89
0.6108
0,5665
42.00
25.50
0,7185
0.6980
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Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Innerrs
(a) SUOT.
Instercce
(b) distance.
200
200
590
(c) w/o alignment.
Figure 11. A case study on the weights a for different alignment methods.
22


Reviews:
[{"review_id": "pt2Ot1XXca", "reviewer": "Reviewer_WSao", "summary": "The authors propose ProWTP, a two-stage method combining prototype learning and optimal transport for watch-time regression prediction and deep recommendation models. First, a hierarchical vector quantized variational autoencoder (HVQ-VAE) is used to convert the continuous label distribution into a high-dimensional discrete distribution, providing credible prototypes for calibration. Then, ProWTP views the alignment between prototypes and instance representations as a Semi-relaxed Unbalanced Optimal Transport (SUOT) problem, with the prototype constraints relaxed. Moreover, ProWTP introduces the assignment and compactness losses to encourage instances to cluster closely around their respective prototypes, enhancing the prototype-level distinguishability. Offline experiments on three industrial databases serve to demonstrate the proposed model performance.\n\n## update after rebuttal\nI acknowledge the authors for their rebuttal. The authors have addressed my main concerns, particularly about clarity in Figure 1, performance improvements, and the lack of qualitative analysis. The planned revisions to figures and the addition of prototype visualizations should significantly enhance the understanding of the proposed method. While the performance gains are modest, the authors have justified their practical significance, especially for medium-to-long videos. Given these improvements, I believe the paper could be accepted and have raised my score to Weak accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear reviewer WSao,\n\nWe greatly appreciate your valuable questions and suggestions, which have helped us improve this paper significantly. We summarize your concerns below and provide detailed responses.\n\n> **Q1: Details of Figures 1 and 2, and subsequent modifications**\n   - Figure 1(a) shows the watch-ratio distributions of three different video duration buckets (short/mid/long) in the Wechat dataset.  \n   - Figure 1(b) provides a dimensionality-reduced visualization of the instance representations $f(x)$ generated by the TR model on the Wechat dataset, using samples from four different video duration buckets to illustrate “instance representation confusion.” Since this figure does not fully convey the core issue of instance representation confusion, we plan to replace it with Figure 4(4) (from the Appendix) in the revised paper to present a clearer illustration in the Introduction. \n   - Figure 2 depicts the two-stage training process of ProWTP:  \n     (1) In the first stage, the watch-ratio distribution $W$ is used as input and reconstructed via the HVQ-VAE. We then extract the parameter $P$ (the codebook) obtained in this process to serve as the prototype parameter for the second stage.  \n     (2) In the second stage, sample features $X$ are used as input, and the output is the predicted $y$.  \n\n   We will enhance Figure 2 with more symbols and annotations in our next revision for better clarity.\n\n---\n\n> **Q2: Experimental improvements and their significance in real-world scenarios**\n\n   - Table 1 reports the average performance over five runs for each model. Generally, for ranking metrics like AUC, an improvement of 0.001 is considered significant. The improvement of 0.01 in RMSE and MAE metrics is significant, which is consistent with previous studies [1].\n   - Our online experiments indicate that different video duration buckets exhibit varying levels of watch-time gain, with **medium-to-long videos** seeing the greatest benefits. This outcome suggests that ProWTP can deliver higher gains in practice by recalibrating samples that are more prone to large prediction errors (i.e., medium and long videos).\n\n|  Duration(s)  |  0-7   |  8-12  | 13-24  | 25-42  | 43-60  | 61-84  | 85-120 | 121-160 | 161-320 | 321-600 |\n| :--------: | :----: | :----: | :----: | :----: | :----: | :----: | ------ | ------- | ------- | ------- |\n| watch time | 0.072% | 0.089% | 0.093% | 0.118% | 0.137% | 0.162% | 0.178% | 0.189%  | 0.213%  | 0.229%  |\n\n[1] CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems.\n\n---\n\n> **Q3: Lack of prototype visualization and error analysis**\n\n   - We observed that the prototypes learned by ProWTP form distinct “striped” clusters in high-dimensional space, and we will add a prototype visualization in the revised version.  \n   - As mentioned in the previous section, ProWTP is particularly effective for medium-to-long videos, which inherently have larger prediction errors. By using prototypes as anchors and bringing samples with large errors closer, the overall performance is improved. Below is RMSE comparisons between TR and ProWTP for different video-duration buckets in the Wechat dataset to illustrate this improvement. According to the proof of A.1, the larger the prediction error, the further its f (x) is from the ideal center, then the larger ASSIGNMENT LOSS will be, and ProWTP will focus on optimizing such samples.\n\n| Bucket | TR    | ProWTP |\n|--------|-------|--------|\n| 0      | 8.62  | 8.57   |\n| 1      | 13.74 | 13.13  |\n| 2      | 21.58 | 20.15  |\n| 3      | 33.42 | 31.95  |\n| 4       | 52.62 | 48.97  |\n---\n> **Q4: Explanation in A.1.1 regarding why only a small portion of training data falls into the non-activation region of ReLU**\n\n   - We tested all three datasets; for each sample $(x, y)$, $y$ is the ground truth, and $ReLU(model(x))$ is the prediction. We found that the number of samples with \\(model(x) < 0\\) (i.e., the activation output being zero) accounted for only about 1–2% of the total data in each dataset.  \n   - This suggests that very few samples lie in the non-activation region of ReLU, supporting our assumption about instance confusion in Appendix A.1. We will include a scatter plot in subsequent revisions to further verify this claim.\n\n---\n\n> **Q5: Revisions to the main text and Appendix linkage to improve readability**\n\nWe appreciate your suggestions and plan to reference Proposition A.1 in Section 4.3. At the same time, we will revise Figure 1(b) to more clearly explain the concept of instance representation confusion.   By doing so, we hope to guide readers to the relevant sections of the Appendix and ensure they can easily find the in-depth explanations presented there.\n\n---\n\nWe sincerely thank you again for your feedback and hope that our responses can change your mind. We look forward to your reply and further discussion.", "claims_and_evidence": "The authors start motivating their research by highlighting the need for video recommendation systems for personalized content, where the user watch-time metric constitutes a key metric for measuring user engagement. They state that existing Watch-Time Prediction (WTP) models have difficulties in achieving a high predictive accuracy across different recommendation systems because they do not consider the multimodal distribution of labels, reflected in Figure 1(a). \n\nThen, the authors also highlight instance representation confusion. Although this issue is illustrated in Figure 1(b), neither a description of the features represented in the 2D scatter plot nor the concept itself (i.e., the authors only refer to “various patterns”) is provided, which makes it difficult to understand this claim.\n\nWhen reaching the Related Work section, i.e., Section 2, the authors properly identify additional drawbacks of existing WTP approaches (e.g., difficult to model ordinal relationships and quantiles, no uncertainty quantification). They also review optimal transport and deep clustering with variational autoencoders (VAEs), identifying those works that constitute the basis of the proposed ProWTP, i.e., Semi-relaxed Unbalanced Optimal Transport (SUOT, see subsequent Section 3. Background) and Vector Quantized Variational Autoencoder (VQ-VAE), respectively.\n\nOverall, the claims made in the paper seem to make sense, but there is a lack of details on the evidence provided in Figures 1(a) and 1(b). Conversely, it should be noted that the Related Work and Background sections are complete and comprehensive, allowing for researchers without experience in WTP, optimal transport, and deep clustering to understand the core of the proposed methodology.", "methods_and_evaluation_criteria": "The proposed method for watch-time prediction is properly designed to overcome the limitations of existing approaches identified at the beginning of the paper. First, the authors make use of prototypes for improving prediction given the multimodal distribution followed by watch-ratio. These prototypes are generated directly from the distribution of the prediction target via the proposed hierarchical VQ-VAE module. Second, to mitigate instance representation confusion, the generated prototypes are used to calibrate instance representations via alignment using SUOT. Finally, training objectives/loss functions are introduced, with additional details provided in the Appendix, important for the sake of reproducibility.\n\nAbout the evaluation criteria, the authors make use of three different databases (Wechat, Kuairand, and Short-video), suitable for the application to be addressed. The performance of the proposed ProWTP is evaluated using well-known metrics such as MAE, RMSE, and AUC extensions for continuous values. Several baselines representing popular WTP methods are considered for comparison purposes.\n\nIn summary, the proposed methods and evaluation criteria make sense for the WTP task.", "theoretical_claims": "A description and a mathematical explanation of instance representation confusion are provided in Appendix A.1. The authors should have referred to this additional information in the main paper, to ensure readers understand this less-known concept. The corresponding proposition seems correct and easy to understand, but the authors do not provide details on the experiments conducted to verify that only a small percentage of the total training data lies in the non-activation region of ReLU.", "experimental_designs_or_analyses": "Reported results include first a comparison with several baselines representing popular WTP in the literature. However, according to Table 1, it should be noted that ProWTP slightly outperforms most of these approaches. A qualitative/error analysis could have been helpful to get a better idea about which situations the proposed method is more advantageous than existing ones, thanks to prototypes and SUOT. The visualization/analysis of the prototypes learned could have also significantly strengthened the discussion of results.  \n\nSecond, an ablation study to measure the impact of the different ProWTP modules is shown in Table 2, supporting the suitability of the design decisions made. Third, different prototype generation methods and distribution alignment methods are tested, reporting results in Tables 3 and 4 to validate the usefulness of the proposed HVQ-VAE and SUOT. It should be noted that the improvements shown are again very small overall, even when compared to classical and simpler K-means/Random methods for clustering, or not using alignment for distribution. The authors should have completed their analysis by focusing on those samples where ProWTP has a clear advantage. Finally, the impact of the number of prototypes learned is shown in Figure 3, again with little differences appreciated between configurations. Although the proposed methodology for ProWTP is convincing, results do not seem to support its significance.", "relation_to_broader_scientific_literature": "The key contributions of the paper integrate concepts from several research areas, including VAEs, deep clustering, and optimal transport, for watch-time prediction, to enhance this task in recommendation systems. The use of hierarchical VQ-VAEs for discrete representation of continuous watch-time distributions aligns with recent advances in prototype learning and quantized representation learning. Moreover, the use of SUOT for aligning instance representations with prototypes extends prior research on transport-based methods for structured prediction tasks.", "essential_references_not_discussed": "Overall, the related works mentioned in the paper seem relevant and enough to understand the context for the main contributions of the paper. Both Related Work and Background sections are complete and comprehensive, identifying advantages and limitations of existing methods.", "other_strengths_and_weaknesses": "The proposed ProWTP method effectively integrates prototype learning with optimal transport in a novel way to enhance watch-time prediction (WTP), addressing key challenges related to prediction accuracy and representation confusion. The paper is well-organized, including a thorough literature review and a clear methodological presentation, which makes it accessible to researchers beyond WTP experts. The evaluation is robust, incorporating multiple datasets and diverse baselines to ensure the reliability of the reported results. Additionally, the supplementary material is comprehensive, covering aspects such as computational complexity and additional experimental validations.\n\nHowever, the reported performance improvements over baselines are relatively small, raising concerns about the practical impact of ProWTP. The experimental section could benefit from qualitative analyses, such as visualizations of learned prototypes and error analyses, to better illustrate when and why ProWTP is most effective. Finally, the clarity of the methodology figures (Figures 1 and 2) could be improved to better align with the textual descriptions, ensuring that the proposed approach is easily understandable.\n\nBased on the strengths and weaknesses of the paper, my current overall recommendation is “Weak reject”.", "other_comments_or_suggestions": "- The authors should have referred to the additional details provided in the Appendix throughout the paper, to ensure readers have a clearer understanding of those concepts which are briefly described in the paper, e.g., instance representation confusion.\n- Figure 1(a): Which database(s) have been used to estimate and show the watch-ratio distribution? A brief description of the nature/contents of the videos considered should be provided at least for better understanding.\n- Figure 1(b): How representation confusion is represented, i.e., which database(s)/features were used for the scatter plot?\n- Figure 1(c) and Figure 2: The quality of the diagrams shown to illustrate the proposed ProWTP could be notably improved, for better comprehension. I think they should be less schematic and more specific on the details of the different stages (watch-ratio distribution, W, meaning of codebook vectors and matrices, etc.). Try to align them with the ProWTP description provided in Section 4 as much as possible. \n- Figure 1 caption should be descriptive, allowing for the understanding of the figure without the need for reviewing the corresponding paragraphs in the paper.\n- Appendix A.1.1: The proposition for instance representation confusion seems correct and easy to understand, but the authors should have provided details on the experiments conducted to verify that only a small percentage of the total training data lies in the non-activation region of ReLU.", "questions_for_authors": "Q1. The reported improvements over baselines are relatively small. Could you provide additional justification for the practical significance of ProWTP? Are there specific scenarios where these gains are particularly meaningful?\n\nQ2. Have you considered including qualitative analyses, such as visualizations of learned prototypes or error breakdowns, to better illustrate when and why ProWTP outperforms other methods? Could such analyses identify specific cases where ProWTP is most effective?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wMZ2ItvnxO", "reviewer": "Reviewer_gDS8", "summary": "This paper presents ProWTP, a novel two-stage approach for predicting user watch-time in video recommendation systems. The method improves prediction accuracy by aligning label distributions with instance representation distributions through prototype learning and optimal transport techniques. Specifically, it leverages a Hierarchical Vector Quantised Variational Autoencoder (HVQ-VAE) and Semi-relaxed Unbalanced Optimal Transport to address the inherent distribution characteristics of labels and mitigate instance representation confusion—challenges often overlooked by existing methods. Experimental results on three datasets demonstrate the effectiveness of ProWTP in enhancing watch-time prediction.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer gDS8,\n\nWe sincerely thank you for recognizing the significance of our work and for your generous positive-score evaluation. We are very grateful for your valuable suggestions and constructive questions, which have helped us improve this paper. Below, we provide detailed responses to your queries:\n\n> **Q1: Lack of detailed analysis regarding the sensitivity to hyperparameters**\n\nIn our method, we have two hyperparameters: K (number of prototypes) and β (weight of the compact loss). In Section 5.2, we have already provided the experimental results and analysis for hyperparameter K. Due to space constraints, here we only supplement the hyperparameter β experiments on the KuaiRand-Pure dataset.\n\n| $\\beta$ |  0.00  |  0.05  |    0.10    |  0.15  |  0.20  |\n| :-----: | :----: | :----: | :--------: | :----: | :----: |\n|  RMSE   | 41.12  | 40.66  | **40.45**  | 40.64  | 40.98  |\n|   MAE   | 24.92  | 24.70  | **24.43**  | 24.68  | 24.89  |\n|  XAUC   | 0.7221 | 0.7258 | **0.7290** | 0.7252 | 0.7223 |\n|  XGAUC  | 0.7004 | 0.7033 | **0.7048** | 0.7036 | 0.7001 |\n\n> **Q2: Minor language and grammatical errors**\n\nThank you for pointing out the writing issues, we will correct it in the final paper.\n\n> **Q3: Complexity analysis and inference efficiency**\n\nUnlike simple direct regression approaches, our method incorporates an additional prototype generation process. But importantly, this prototype generation is completely independent, and its spatio-temporal complexity does not affect the training and inference time of ProWTP. When the distribution of watch ratios is sufficiently large, the resulting prototype distribution remains stable, which means that Stage I does not need to be performed frequently.\n\nIn Stage II, during the inference phase, the OT module is removed. The final value is computed as a linear combination of similarities to each prototype, which is then input into the regressor. The time complexity of this process is only O(CK), where C and K are small constants, ensuring that the time overhead remains negligible. \n\n\n\n\n> **Q4: online experiments environments details**\n\nThank you for your suggestion. Industrial recommendation systems typically employ a cascading architecture consisting of four stages: recall, pre-ranking, ranking, and re-ranking—a structure designed to efficiently recommend items to users from massive pools of video candidates. Since the recall stage operates without stringent real-time requirements, we strategically deployed ProWTP in this layer of an online short-video recommendation system, where it functions as one of multiple recall paths.\n\nTo validate its effectiveness in real-world applications, we used D2Q as our baseline model for comparison. The experimental results convincingly demonstrate the significant performance improvements our method delivers in actual business scenarios.\n\n> **Q5: deal with imbalanced data distributions**\n\nThanks for your question. In fact, video recommendation scenarios inherently face imbalanced data distributions in terms of video duration. Most video datasets, including WeChat and KuaiRand, exhibit left-skewed distributions in durations. Our experimental results clearly demonstrate that ProWTP outperforms competing models on the WTP task, showing particularly strong performance in watch time prediction under imbalanced data distribution conditions.\n\n\n\nThank you once again for your valuable suggestions and feedback. If you have any further questions, we would be happy to continue the discussion with you. We are looking forward to your reply.", "claims_and_evidence": "Yes, this paper has strong experimental and theoretical evidence to support the claim.", "methods_and_evaluation_criteria": "Yes, this paper used two public datasets and one private dataset with a reasonable training-testing-validation setup. The use of widely recognized metrics (MAE, RMSE, XAUC, XGAUC) ensures the results' validity and comparability with existing research.", "theoretical_claims": "Yes, I check the theoretical claims and can confirm the correctness.", "experimental_designs_or_analyses": "Yes, The experiments look solid. The ablation studies and comparative analyses effectively validate the contribution of each proposed component (HVQ-VAE, SUOT, assignment loss, compactness loss). More experiment results can be found in appendix.", "relation_to_broader_scientific_literature": "The ProWTP framework addresses a key limitation of existing watch-time prediction approaches, which typically transform watch-time labels for prediction and then reverse the transformation, neglecting both the natural distribution properties of labels and the instance representation confusion that can lead to inaccurate predictions.\n \nBeyond improving the accuracy of video recommendations, this work bridges multiple subfields by offering insights into generalized regression problems, multimodal learning, and structured representation alignment in AI models. Its implications extend beyond recommendation systems, potentially influencing a broader range of machine learning applications that require distribution alignment and representation calibration.", "essential_references_not_discussed": "No essential references appear to have been omitted. The paper comprehensively cites and engages with relevant prior works.", "other_strengths_and_weaknesses": "S1: This paper presents a completely different perspective to optimize the WTP, proposing phenomena and methods that are very interesting and can be extended to more application scenarios.\n\nS2: The hierarchical VQ-VAE-based credible prototype generation is innovative, providing clear advantages in capturing multimodal distribution patterns.\n\nS3: Distribution alignment effectively mitigates the representation confusion issue, leading to improved predictive performance.\n\nW1: Lack of detailed analysis regarding the sensitivity to hyperparameters, especially in different dataset scenarios.\n\nW2: Minor language and grammatical errors should be revised for enhanced readability.\n\nOverall: I think this paper is very interesting and the proposed phenomenon of multi-peaked distributions is valuable. And the scheme of conversion to prototype by distribution is very innovative.", "other_comments_or_suggestions": "* Minor typos are observed (e.g., \"Sine\" instead of \"Since\" in Section 5.1). Thorough proofreading is recommended.\n\n* In Appendix, the authors have a typo: (see line 1074), A9 and 10 are the same thing", "questions_for_authors": "* Can authors discuss the computational efficiency of ProWTP, particularly in large-scale production environments?\n* How sensitive is ProWTP to the hyperparameters (e.g., $\\lambda$ in SUOT)? A brief analysis or guidance on hyperparameter tuning would enhance the practical applicability.\n* Provide more online experiments environment will be better.\n* How does ProWTP handle cases with highly imbalanced data distributions, such as scenarios with rare but critical user behavior patterns?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QMYy2Ec84s", "reviewer": "Reviewer_3cw2", "summary": "This paper focuses on the watch-time prediction problem in video recommender systems. It employs a two-stage framework: (1) using a hierarchical vector quantized variational autoencoder to generate credible prototypes from watch-ratio distributions; and (2) leveraging semi-relaxed unbalanced optimal transport to align samples with those prototypes. The goal is to alleviate “instance representation confusion” that leads to prediction errors. Appendix gives the corresponding mathematical proof and statistical analysis that justifies the motivation of the paper. This paper provides offline experiments and online A/B tests to verify the effectiveness of the method in real scenarios. Overall, this paper has a very fresh perspective, is undeniably innovative, and presents interesting ideas and scenarios.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for recognizing the significance of our work and for your generous positive-score evaluation. We are very grateful for your valuable suggestions and constructive questions, which have helped us improve this paper. Below, we provide detailed responses to your queries:\n\n> **Q1: modeling complexity(two-stage framework vs simple direct regression approaches)(refer to A.4)**\n\nDue to space constraints, please refer to the answer to Q3 in the rebuttal for Reviewer gDS8.\n\n> **Q2:  analysis of assign loss and compact loss**\n\nDuring Stage II, we adopt two losses to help with calibrating the sample space. In our method, we aims for the instance representations to cluster tightly around their corresponding prototypes. So the assign loss is designed to decrease the distance between samples and their corresponding prototypes. And since we hope for instances assigned to the same prototype to be closer together in the representation space, the compact loss is  designed to encourage samples under the same prototype to cluster more closely in the representation space\n\nBy minimizing both losses, our method can not only help reduce instance representation confusion but also enhance the model’s ability to capture fine-grained features, ultimately improving prediction performance.\n\n\n\n\n> **Q3:  Model adjustment for changes in user behavior (refer to A.4)**\n\nWe consider the following two aspects:\n\n(1) The stability of user behavior\n\nHVQ-VAE is completely independent, and its spatio-temporal complexity does not affect the training and inference time of ProWTP. When the distribution of watch ratios is sufficiently large, the resulting prototype distribution is stable. Furthermore, we observed that for a well-established video recommendation APP, the watch-ratio distribution remains largely unchanged and consistent across multiple months.\n\nAs shown in Figure 10, we randomly sampled 200,000 users from our APP (a short-video platform) and extracted their historical behavior on the 1st day of each month from January to November 2024. The data were divided into D=15 buckets based on video duration. We then computed the Wasserstein Distance between the watch-ratio probability density distributions of each month and November, as well as the Kolmogorov-Smirnov test with $p<0.05$ between their cumulative empirical distributions. The results indicated no significant distribution shifts across multiple months.\n\n(2) Model adjustments driven by user behavior changes\n\nEven in extreme scenarios where user behavior undergoes notable adjustments, we only need to resample the watch-ratio distributions for each duration buckets, perform offline retraining, and update the weights of ProWTP. This process incurs minimal computational overhead.\n\n> **Q4: more findings from the online A/B test**\n\n|  Duration  |  0-7   |  8-12  | 13-24  | 25-42  | 43-60  | 61-84  | 85-120 | 121-160 | 161-320 | 321-600 |\n| :--------: | :----: | :----: | :----: | :----: | :----: | :----: | ------ | ------- | ------- | ------- |\n| watch time | 0.072% | 0.089% | 0.093% | 0.118% | 0.137% | 0.162% | 0.178% | 0.189%  | 0.213%  | 0.229%  |\n\nAccording to the results, we observed two key findings:\n\n1. ProWTP demonstrated consistent performance improvements over D2Q across all duration buckets.\n2. When examining different duration segments, we found that medium and long-form videos (>60s) exhibited significantly more pronounced watch time benefits compared to shorter videos. This pattern also aligns with the results observed in our offline dataset evaluations.\n\n> **Q5: alignment  visualization**\n\nWe visualized the relationship between errors and instance representations in Appendix A.1 (Figures 4 and 5). By comparing these visualizations, we observed that: \n\n1. Prediction error ∆ is positively correlated with the degree of confusion. \n2. TR exhibits a significantly higher level of confusion, while ProWTP effectively mitigates this confusion by reducing the distance between instances and reliable prototypes. \n3. Compared to TR, ProWTP shows significantly fewer points with large errors. \n\nThis demonstrates that the root cause of reducing prediction errors lies in learning better instance representations.\n\n> **Q6: Symbol list**\n\nThank you for your suggestion. Due to space constraints of rebuttal, we will add a list of symbols in the final paper.\n\nThank you once again for your valuable suggestions and feedback. If you have any further questions, we would be happy to continue the discussion with you. We are looking forward to your reply.", "claims_and_evidence": "The authors argue that conventional methods fail to fully exploit the multi-modal nature of watch-ratio under different video-duration buckets and often overlook the confusion that arises in deep recommendation model representations. By generating credible prototypes on the label side and performing distribution alignment, their method substantially reduces representation offset, thereby lowering prediction errors. Empirical evidence is provided by experiments on multiple real-world industrial datasets, where the proposed approach outperforms a wide range of baselines, demonstrating its effectiveness.", "methods_and_evaluation_criteria": "The papers proposed two-stage approach involves:\n1. Using HVQ-VAE to quantize the watch-ratio distribution into prototypes for model calibration;\n2. Employing SUOT to align sample representations with these prototypes, along with an assignment loss and a compactness loss to further ensure samples cluster around their respective prototypes.\nEvaluation uses RMSE and MAE to measure regression errors, and XAUC and XGAUC to assess ranking performance.", "theoretical_claims": "Appendix A.1 provides the key mathematical derivation, proving that watch-time prediction error $\\Delta$ is positively correlated with $\\|f(x) - \\mu_y\\|$.  The proof views the network’s prediction as $\\mathrm{ReLU}(W f(x) + b)$ and assumes there exists a center $\\mu_y$ related to the true value. Under this assumption, closeness of f(x) to $\\mu_y$ yields smaller prediction errors. The mathematical discussion, split according to the activation regions of ReLU, supports the approach of mitigating confusion by aligning representations to credible prototypes.", "experimental_designs_or_analyses": "The authors conduct experiments on multiple real-world datasets, comparing with various watch-time prediction baselines (like D2Q, TPM) and debiasing models (like DVR, CWM). They also perform ablation studies to assess how removing HVQ-VAE, SUOT, or the assignment loss affects performance. Additional visualizations illustrate how the representation space becomes significantly less confused once samples are attracted to their respective prototypes.", "relation_to_broader_scientific_literature": "This work applies vector-quantized variational autoencoders and distribution alignment via optimal transport to the recommendation context, extending beyond prior watch-time methods that rely on simplistic bucketization or direct regression. It proposes an effective scheme to tackle instance confusion via prototype calibration, particularly suited to scenarios featuring multi-modal label distributions.", "essential_references_not_discussed": "I do not find such related works.", "other_strengths_and_weaknesses": "Strengths:\n1. The paper proposes a novel and interesting two-stage method that stands out by quantizing label distributions themselves—rather than relying on solely sample-based embeddings—to derive meaningful prototypes for instance representation calibrations.\n2. A Well-Motivated paper.  By focusing on “instance representation confusion,” the authors provide both an intuitive and a formal rationale for aligning sample representations with label-derived prototypes\n3. The paper presents experiments on multiple real-world datasets as well as online ab test, systematically comparing the proposed method with a range of established baselines. \n4. In addition to empirically validating their approach, the authors incorporate a rigorous theoretical analysis, particularly in Appendix A.1, where they link prediction error to the distance from an “ideal center.” This mathematical proof underpins why the prototype alignment mechanism effectively curbs representation confusion and improves watch-time prediction accuracy.  \n\nWeakness:\n1. The proposed two-stage framework may introduce additional modeling complexity compared to simple direct regression approaches, can you provide more details?  \n2. While the paper provides a solid exploration of assignment and compactness loss functions, it would be insightful to see a deeper analysis of how these losses interact with other potential regularizers.  \n3. It would be beneficial to include a broader discussion on how prototypes evolve when user behavior changes over time or when new content is introduced. Realistic settings often involve rapidly shifting distributions, and observing how HVQ-VAE adapts (or could be adapted) would strengthen the paper’s practical insights.  \n4. The online experiment only includes results in Appendix, plz sharing more findings from an online A/B test would highlight how the proposed method translates to real-world gains, offering more confidence to practitioners.  \n5. This paper briefly touches on how prototype alignment can alleviate “representation confusion.” More extensive visualizations or case studies showing how specific user-video pairs move in representation space before and after alignment would further clarify this mechanism.  \n6. Adding a list of symbols can help others better understand the content.", "other_comments_or_suggestions": "I have no other comments and suggestions.", "questions_for_authors": "Please refer to the weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "I8cuMN2Hrc", "reviewer": "Reviewer_KkGb", "summary": "The paper proposes ProWTP, a two-stage approach designed to enhance watch-time prediction in video recommender systems by integrating prototype learning with optimal transport (OT). In the first stage, ProWTP employs a hierarchical vector quantized variational autoencoder (HVQ-VAE) to transform continuous watch ratio labels into discrete prototypes. In the second stage, optimal transport is used to align the distribution of these labels with the instance representation distribution, thereby boosting prediction accuracy. Extensive offline experiments conducted on three datasets show that ProWTP outperforms existing methods.\n\n#########\nAdded after rebuttal: I checked the authors' rebuttal, and gave a detailed response. In summary, the authors' feedback doesn't resolve my concerns, so I maintain my original ranking of the paper.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer KkGb,\n\nWe appreciate your valuable questions and suggestions. We summarize your concerns below and provide responses. \n\n> **Q1: The motivation is clear and explicit.**  \n\nWe mention that existing WTP models struggle to achieve high accuracy. We believe the main reason lies in instance representation confusion, which we explain from two perspectives:\n\n1. Mathematical Explanation\n\n let the instance representation of a sample $(x,y)$ be $f(x)$, with its ideal center being $ \\mu_y = \\mathbb{E}[f(x) \\mid y]$, where $y$ is the ground-truth. The degree of instance representation confusion is defined as the distance between the instance representation and the ideal center $ d(f(x), \\mu_y) = \\|f(x) - \\mu_y\\|.$ Then, the model's prediction error $ \\Delta_x = |y - \\hat{y}|$ is predominantly correlated with the degree of instance representation confusion $d(f(x), \\mu_y)$.\n\n2. Model Performance\n\nIn existing WTP models, the instance representation space is often disorganized, with instance representations of different data types failing to form well-defined clusters, as shown in Figure4. Also, we provide additional results of different duration buckets in **Reviewer WSao Q3**. ProWTP's performance is even better in medium and long videos with large prediction errors, which further illustrates that ProWTP optimizes the distance between instance representations and prototypes to achieve more accurate predictions.\n\nWe believe that ProWTP improves prediction accuracy primarily by alleviating instance representation confusion and directly incorporating label distributions into the model, thereby providing additional information gain. So the motivation is clear and explicit.\n\n> **Q2: Suitable for any deep recommendation model** \n\nThis statement wants to show that ProWTP is a model-independent scheme, i.e., $f$ in $f(x)$ can be replaced by any depth model (i.e. DCN), and there is no overclaim.\n\n> **Q3: Comparison with traditional clustering** \n\nIn Table 3, we compare Kmeans' scheme for generating prototype. As for the clustering-based contrastive learning scheme, we have not searched relevant literature in WTP at this time.\n\n> **Q4: Efficiency**\n\n**A.4.** has discussed this problem in details,  OT is only present during training, OT is removed during testing, and online inference time complexity is linear O(CK).\n\n> **Q5: lack of references** \n\n**Its not TRUE. line 096 in Related work part, [1] proposes bimodal distribution, which we have already cited**. In our work, we focus on the multi-modal distribution characteristics of videos. Previous studies have overlooked the distribution of watch-time beyond the video duration, and we are the first to propose focusing on the multimodal characteristics rather than just bimodal distribution. Importantly, we need to emphasize that the main contribution of our paper is the transformation of label distributions into prototypes that provide a credible reference for model calibrations.\n\n> **Q6: Visualization** \n\nThanks for your suggestion, we will add the use of kmeans-generated prototypes for ProWTP visualization subsequently.\n\n> **Q7: Rationale for OT**\n\n**In Appendix A.12, we compare three different alignment methods: OT, L2 distance, and no alignment.** We present their results and conduct a case study. The OT alignment method performs global alignment rather than aligning each sample independently. Compared to other alignments, OT introduces structured information. This approach not only achieves better results but also produces sparser weights between instances and prototypes.\n\n> **Q8: Efficiency Trade-off**\n\n**A.4.** has discussed this problem in details.  We tested the wr distribution across months and its distribution does not change significantly, so it is reasonable that prototype is naturally fixed. **If it changes in extreme cases, we just need to re-pull the data for training, as is always done in industry.**\n\n> **Q9: Robustness Across Scenario**\n\nThis scenario does not occur in the WTP task, because according to our platform data analysis (its DAU is 400 millions), **the users' watch-ratio always shows a multi-modal distribution. This is inherent in the WTP.**\n\n> **Q10: only two datasets**  \n\n**ITS NOT TRUE**.  In our paper it is **three datasets** and the results are reported averaged over five runs. \n\n> **Q11. The interpretability of prototypes and their association with user behavior .** \n\nIt has been discussed in **A.3** in details.  Due to space constraints, you can refer to A.3.\n\n> **Q12: More baselines**\n\n| Wechat | CREAD | SWAT  | ProWTP |\n|--------|-------|-------|--------|\n| RMSE   | 28.93 | 29.31 | 28.47  |\n\nThe time of GR in arxiv is Sat, 28 Dec 2024, which is a same-time work and can be disregarded.\n\n> **Q13: Figures modification**\n\n Due to space constraints, you can refer to Reviewer WSao Q1.\n\nWe sincerely thank you again for your feedback and hope that our responses can change your mind. We look forward to your reply and further discussion.", "claims_and_evidence": "No. This work is problematic in at least the following aspects: \n\n1. Unclear Motivation: The submission does not adequately justify the motivation of the proposed methods (e.g. D2Q for debiasing, TPM for reducing classification difficulty). The claim, \"However, those methods struggle to consistently maintain high predictive accuracy across different models,\" was made without detailed explanation or evidence. The authors should strengthen the motivation of their approach.\n\n2. Overgeneralized Claim: The assertion that the method is \"suitable for any deep recommendation model\" (in line 52) is questionable. In scenarios where data lacks multimodal distributions, the model’s robustness is uncertain, and no evidence addresses this limitation.\n\n3. Insufficient Comparison: The authors claim, \"Different from traditional prototype learning, ProWTP generates prototype vectors from label distributions, providing models with more precise and credible calibration references.\" (in line 81) While metrics are provided in tables, the superiority over simple clustering-based contrastive learning (which could also handle multimodal predictions) is not convincingly demonstrated. Visualizations comparing ProWTP with clustering, beyond the TR vs. ProWTP in the appendix, would enhance credibility.\n\n4. Efficiency Concerns: The use of optimal transport likely incurs high computational cost, yet the efficiency of the proposed two-stage online approach is not adequately addressed, raising doubts about its practicality.\n\n5. Lack of Literature Review: The paper lacks a thorough review of prior watch time prediction research and omits key references. Notably, the claim, \"We investigate the multimodal distribution properties of watch-ratio across different video duration buckets for the first time,\"(in line 105) is inaccurate, as the bimodal distribution of KuaiRand was previously studied in Zhao et al., 2023.\n\n[1] Zhao et al., 2023, Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "Yes.", "experimental_designs_or_analyses": "Yes, I checked the soundness/validity of any experimental designs or analyses. I have the following concerns:\n1. Visualization in Appendix: While the appendix visualizes a comparison between TR and ProWTP, it lacks a comparison with clustering-based methods. Including such a comparison (e.g., ProWTP vs. clustering) would strengthen the evidence for ProWTP’s superiority, as clustering could also address multimodal distributions, making the current visualization less convincing.\n\n2. Rationale for Optimal Transport: It is unclear why aligning the feature space with prototype vectors via optimal transport improves watch time prediction. Simpler alternatives (e.g., cosine similarity or k-means) might achieve similar alignment. The authors should justify the choice of optimal transport and explain its specific benefits for the prediction task.\n\n3. Efficiency Trade-offs: The trade-off between performance gains and computational efficiency is concerning. Although the authors sample 20% of the data for contrastive learning, the per-sample cost of optimal transport appears substantial. Additionally, the deployment strategy is ambiguous: Are prototypes fixed during online inference? If so, how does the model address distribution shifts, and what are the implications for robustness?\n\n4. Robustness Across Scenarios: The claim \"suitable for any deep recommendation model\" is tested only on multimodal data. The experimental design lacks validation on scenarios without multimodal distributions (e.g., unimodal data), leaving the model’s generalizability and robustness unverified.", "relation_to_broader_scientific_literature": "The contributions of the paper were built upon and extending several strands of work in the scientific literature:\n1.Prototype Learning and Representation Discretization: Based on methods like VQ-VAE, the paper extends unsupervised learning and clustering techniques by converting continuous labels into discrete prototype.\n\n2.Optimal Transport for Distribution Alignment: Optimal transport has been widely used to address distribution mismatches. Here, it is employed to align the label-based prototype distribution with instance representations for for video watch-time prediction", "essential_references_not_discussed": "1. The paper does not provide a thorough review of prior research on watch time prediction and omits several important references. Specifically, the claim in line 105, \"We investigate the multimodal distribution properties of watch-ratio across different video duration buckets for the first time,\" is inaccurate. The bimodal distribution of KuaiRand was previously explored in [1], undermining the novelty asserted by the authors.\n\n2. Additionally, the paper lacks comparisons with key prior works, including:\nCREAD[2], SWAT[3], GR[4].\n\n3. The reported improvements over baselines on the two datasets appear modest, warranting a statistical significance test to validate their impact. To strengthen the evaluation, the authors should compare their method against these approaches and test performance on additional benchmarks such as KuaiRec and CIKM datasets.\n\n4. The paper lacks discussion on interpreting prototypes and their connection to user behavior, which could hinder insight into the model’s predictions.\n[1] Zhao et al., 2023, Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation\n[2] CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems\n[3] SWaT: Statistical Modeling of Video Watch Time through User Behavior Analysis\n[4] Generative Regression Based Watch Time Prediction for Video Recommendation", "other_strengths_and_weaknesses": "Figure 1b fails to specify the method used or which data components are analyzed to demonstrate \"Representation confusion.\" In Figure 2, the boundaries and logic between modules are poorly defined and highly confusing. Overall, the quality of the figures is substandard, detracting from the paper’s readability and professionalism.", "other_comments_or_suggestions": "No.", "questions_for_authors": "Please refer to the problems above for detailed weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents a novel, well-motivated method for improving video watch-time prediction via prototype-based calibration and structured alignment. The approach is theoretically well-founded, empirically validated on large-scale datasets, and deployed in a real-world industrial system. While quantitative improvements are modest, the novelty and deployment practicality offer considering contributions to application-driven ML.

### Strengths
- Clear Motivation and Mathematical Justification: The paper addresses “instance representation confusion”—a unique perspective on model error origins—and provides a theoretical proposition linking prediction error to prototype alignment. Reviewers appreciated the clarity and rigor of this formulation.
- Method Novelty: The paper presents a novel two-stage framework (ProWTP) that integrates prototype learning with optimal transport (OT) for calibrated watch-time prediction. By leveraging hierarchical vector-quantized VAEs (HVQ-VAE), it transforms continuous labels into discrete prototypes, which are aligned with instance representations using semi-relaxed unbalanced OT. This structure is both theoretically grounded and practically effective.
- Extensive Experiments: The ProWTP is evaluated across three real-world industrial datasets, with thorough ablation studies, hyperparameter sensitivity analyses, and online A/B tests. These demonstrate both empirical efficacy and deployment readiness. The approach shows particular benefit on medium-to-long videos, which typically suffer from higher prediction variance.

Considering minor presentation issues and one problematic reviewer, the majority of reviewers lean to acceptance, with at least two increasing their scores post-rebuttal.
Decision: Accept (poster)