Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation

• URL: http://arxiv.org/abs/2407.12223v1
• Date: Wed, 17 Jul 2024 00:25:35 GMT
• Title: Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation
• Authors: Chengzhi Lin, Shuchang Liu, Chuyuan Wang, Yongqi Liu,
• Abstract summary: We introduce a novel estimation technique -- Conditional Quantile Estimation (CQE) CQE utilizes quantile regression to capture the nuanced distribution of watch time. We also design several strategies to enhance the quantile prediction including conditional expectation, conservative estimation, and dynamic quantile combination.
• Score: 2.3166433227657186
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Within the domain of short video recommendation, predicting users' watch time is a critical but challenging task. Prevailing deterministic solutions obtain accurate debiased statistical models, yet they neglect the intrinsic uncertainty inherent in user environments. In our observation, we found that this uncertainty could potentially limit these methods' accuracy in watch-time prediction on our online platform, despite that we have employed numerous features and complex network architectures. Consequently, we believe that a better solution is to model the conditional distribution of this uncertain watch time. In this paper, we introduce a novel estimation technique -- Conditional Quantile Estimation (CQE), which utilizes quantile regression to capture the nuanced distribution of watch time. The learned distribution accounts for the stochastic nature of users, thereby it provides a more accurate and robust estimation. In addition, we also design several strategies to enhance the quantile prediction including conditional expectation, conservative estimation, and dynamic quantile combination. We verify the effectiveness of our method through extensive offline evaluations using public datasets as well as deployment in a real-world video application with over 300 million daily active users.

Related papers

• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
  Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs. We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint. We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
  arXiv  Detail & Related papers  (2024-06-05T13:36:38Z)
• Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees [2.1684857243537334]
  This tutorial focuses on efficient methods to predictive monitoring (PM) PM is the problem of detecting future violations of a given requirement from the current state of a system. We present a general and comprehensive framework summarizing our approach to the predictive monitoring of CPSs.
  arXiv  Detail & Related papers  (2023-12-04T15:16:42Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Uncertainty Estimation based on Geometric Separation [13.588210692213568]
  In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. We put forward a novel geometric-based approach for improving uncertainty estimations in machine learning models.
  arXiv  Detail & Related papers  (2023-01-11T13:19:24Z)
• ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference [54.17205151960878]
  We introduce a sampling-free approach that is generic and easy to deploy. We produce reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost.
  arXiv  Detail & Related papers  (2022-11-21T13:23:09Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting [30.277213545837924]
  Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. In this work, we consider the time-series data as a random realization from a nonlinear state-space model. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings.
  arXiv  Detail & Related papers  (2021-06-10T21:49:23Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets [0.0]
  A novel approach for the construction and training of time series models is presented. The proposed method is appropriate for constructing predictive models for non-stationary time series.
  arXiv  Detail & Related papers  (2020-11-23T21:28:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.