---
title: ViSeDOPS in Short-Video Recommendations
url: https://www.emergentmind.com/topics/visedops
type: topic
---

# ViSeDOPS in Short-Video Recommendations

ViSeDOPS, in the context of short-video recommendations, refers to frameworks and methodologies that leverage debiased, multi-semantic label extraction from dense watch-time feedback, using causal inference techniques and quantile-based stratification. Such approaches move beyond direct use of raw watch-time, addressing the central biases and variabilities inherent in user engagement metrics and enabling robust optimization of recommendation models while aligning with the core ranking objectives of content surfacing platforms.

## 1. Limitations of Direct Watch-time Regression

Raw watch-time $T_{u,i}$, where $u$ denotes user and $i$ denotes item, is the primary feedback signal in large-scale short-video recommendation scenarios, modeled as a sample from a joint distribution $P(T|u,i)$. The naive approach minimizes mean-squared error:
$$
L_{\mathrm{MSE}} = \mathbb{E}_{u,i} [(T_{u,i} - f(u,i; \theta))^2]
$$
for a parametric recommender $f(\cdot; \theta)$. However, this strategy is fundamentally limited by two factors:

- **Heavy-tailed Distribution and Outlier Sensitivity:** Watch-time exhibits a long-tailed distribution, with most videos watched for seconds but rare items accruing hours via repeat plays. MSE optimization becomes skewed toward outliers, resulting in unstable learning.
- **Confounding and Bias Due to Video Attributes:** $T_{u,i}$ is not solely reflective of latent user interest $M_{u,i}$; it is confounded by video-side attributes, most notably video duration $V_d$ and popularity. The relevant causal structure:
  $$
  V_d \rightarrow T_{u,i} \leftarrow M_{u,i} \rightarrow (u,i)
  $$
  induces direct and indirect pathways from $V_d$ to $T_{u,i}$, such that models trained directly on $T_{u,i}$ systematically overpreference longer videos regardless of user interest [2306.17426].

## 2. Quantile-based Multi-Semantics Labeling

To mitigate the influence of outliers and align labels with ranking goals, watch-time is transformed into quantile-based discrete semantics:

- Let $F_T$ be the empirical CDF of $T$ in the training data.
- The support of $T$ is partitioned into $K$ intervals by quantiles:
  $$
  Q_k = F_T^{-1}\left(\frac{k}{K}\right),\quad k=0,1,\ldots,K; \quad Q_0=0,\ Q_K=\infty
  $$
- The label vector $y_{u,i}^{(k)}$ is assigned as:
  $$
  y_{u,i}^{(k)} = 
  \begin{cases}
  1, & Q_{k-1} \leq T_{u,i} < Q_k \\
  0, & \text{otherwise}
  \end{cases}
  $$
  for $k=1,\ldots,K$

Common label semantics include:

| Label Type                     | Definition                                                    | Purpose                                   |
|-------------------------------|---------------------------------------------------------------|-------------------------------------------|
| Watch-time Percentile Rank (WPR)| $y_{wpr} = \sum_{k'=1}^k 1/K$ for bin $k$                   | Soft percentile, $[0,1]$ scale            |
| Effective View (EV)            | $y_{ev} = \mathbb{I}(T_{u,i} \geq t_{50})$                  | Binary: watches above median               |
| Long View (LV)                 | $y_{lv} = \mathbb{I}(T_{u,i} \geq t_{75})$                  | Binary: watches above $75^{th}$ percentile |

This quantile-based transformation prioritizes relative ordering of watch times, stabilizes learning, and aligns loss functions with ranking performance metrics.

## 3. Causal Label Debiasing via Stratified Quantiling

Despite quantile transformation, label bias from confounders, especially $V_d$, persists. To neutralize this, causal adjustment is performed using the backdoor adjustment formula:
$$
\mathbb{E}[T_{u,i} | do(M_{u,i})] = \sum_{v_d} P(v_d) \;\mathbb{E}[T_{u,i}\mid M_{u,i}, V_d = v_d]
$$

Practical debiasing is achieved by stratifying the dataset by $v_d$ (video duration buckets), then performing quantile mapping _within_ each stratum:
- For each $(u,i) \in D^{v_d}$ (duration bucket), labels are defined as:
  $$
  y_{wpr}^d(u,i) = g_{wpr}\big((u,i,T_{u,i}); D^{v_d}\big)
  $$
  where $g_{wpr}$ is quantile assignment in $D^{v_d}$.
- Likewise for binary labels:
  $$
  y_{ev}^d(u,i) = \mathbb{I}(T_{u,i} \geq t_{50}(D^{v_d}))
  $$
  $$
  y_{lv}^d(u,i) = \mathbb{I}(T_{u,i} \geq t_{75}(D^{v_d}))
  $$

Further stratification by user/item feature hashes can mitigate additional confounding (popularity, user activity). This produces uniform label distributions per bucket, eliminating the direct $V_d$ effect [2306.17426].

## 4. Model Integration and Multi-Task Objective

The debiased, multi-semantic labels are incorporated in a multi-task deep recommender using the Multi-gate Mixture-of-Experts (MMoE) framework:

- Shared embedding and expert layers feed into separate “heads” for each label type:
    - $f_{wpr}(u,i;\theta)$, $f_{ev}^d(u,i;\theta)$, $f_{ev}^v(u,i;\theta)$, $f_{ev}^u(u,i;\theta)$
    - $f_{lv}^d(u,i;\theta)$, $f_{lv}^v(u,i;\theta)$, $f_{lv}^u(u,i;\theta)$

The global loss is a sum of weighted per-label losses:
\[
L(\theta) = L_{wpr} + \lambda_{ev} (L_{ev}^d + L_{ev}^v + L_{ev}^u) + \lambda_{lv} (L_{lv}^d + L_{lv}^v + L_{lv}^u)
\]
Per-task loss formulations:
- $L_{wpr} = \mathbb{E}[(f_{wpr}(u,i) - y_{wpr}^d(u,i))^2]$
- $L_{ev}^* = -\mathbb{E}\big[y_{ev}^*(u,i) \log\sigma(f_{ev}^*(u,i)) + (1-y_{ev}^*(u,i)) \log(1-\sigma(f_{ev}^*(u,i)))\big]$
- Analogous for $L_{lv}^\ast$

The $\lambda$ weighting parameters are tuned to balance gradient magnitudes across tasks. No architectural or backward-compatibility changes are needed; all debiasing occurs at the label stage.

## 5. Empirical Comparison and Performance

The debiased, multi-semantics labeling approach was benchmarked against several strong baselines:
- TR: direct watch-time regression
- WLR: YouTube’s weighted-logistic baseline
- OR: ordinal regression on $T$
- D2Q: duration-adjusted quantile regression

Metrics comprised both ranking (AUC, GAUC) and regression error (MAE, MAPE, RMSE). On a 1-hour holdout:
| Method | AUC   | GAUC  | MAE   | MAPE  | RMSE  |
|--------|-------|-------|-------|-------|-------|
| D2Q    | 0.6732| 0.6581|22.67  |2.73   |45.53  |
| DML    | 0.6763| 0.6617|21.76  |2.60   |42.77  |

Online A/B tests over 5 days on Kuaishou’s major surfacing channels demonstrated uplifts relative to WLR:
- Featured-Video Tab: Watch Time +2.23%, App Usage +0.77%
- Slide Tab: Watch Time +2.06%, App Usage +0.71%

DML’s empirical gains validate the efficacy of extracting multiple semantics, using relative quantile labels, and applying label-construction-time debiasing—all without changes to the deployed recommender backbone [2306.17426].

## 6. Context and Implications

This line of work demonstrates that nuanced, causally debiased label engineering—coupled with multi-task learning—can rectify core deficiencies in dense implicit-feedback recommendation where only watch-time is available. Operating at the label level enables robust, architecture-agnostic bias correction that is compatible with production-scale recommender systems. The stratified quantiling mechanism is extensible to de-confounding against additional side information. A plausible implication is that these methodological principles may generalize to other domains with heavy-tailed, confounded feedback signals, beyond short-video and media recommendation.

Source: https://www.emergentmind.com/topics/visedops