Yang Song

Kunpeng Li, Guangcui Shao, Naijun Yang et al.

Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business. It is widely used in a variety of business scenarios to make operational decisions when acquiring new customers. Modeling LTV is a challenging problem, due to its complex and mutable data distribution. Existing approaches either directly learn from posterior feature distributions or leverage statistical models that make strong assumption on prior distributions, both of which fail to capture those mutable distributions. In this paper, we propose a complete set of industrial-level LTV modeling solutions. Specifically, we introduce an Order Dependency Monotonic Network (ODMN) that models the ordered dependencies between LTVs of different time spans, which greatly improves model performance. We further introduce a Multi Distribution Multi Experts (MDME) module based on the Divide-and-Conquer idea, which transforms the severely imbalanced distribution modeling problem into a series of relatively balanced sub-distribution modeling problems hence greatly reduces the modeling complexity. In addition, a novel evaluation metric Mutual Gini is introduced to better measure the distribution difference between the estimated value and the ground-truth label based on the Lorenz Curve. The ODMN framework has been successfully deployed in many business scenarios of Kuaishou, and achieved great performance. Extensive experiments on real-world industrial data demonstrate the superiority of the proposed methods compared to state-of-the-art baselines including ZILN and Two-Stage XGBoost models.

Akshit Sarpal, Qiwen Kang, Fangping Huang et al.

Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets

Cheng Lu, Yang Song

Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hyperparameters and are prone to discretization errors. While continuous-time formulations can mitigate these issues, their success has been limited by training instability. To address this, we propose a simplified theoretical framework that unifies previous parameterizations of diffusion models and CMs, identifying the root causes of instability. Based on this analysis, we introduce key improvements in diffusion process parameterization, network architecture, and training objectives. These changes enable us to train continuous-time CMs at an unprecedented scale, reaching 1.5B parameters on ImageNet 512x512. Our proposed training algorithm, using only two sampling steps, achieves FID scores of 2.06 on CIFAR-10, 1.48 on ImageNet 64x64, and 1.88 on ImageNet 512x512, narrowing the gap in FID scores with the best existing diffusion models to within 10%.