SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games

• URL: http://arxiv.org/abs/2506.10487v1
• Date: Thu, 12 Jun 2025 08:42:11 GMT
• Title: SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
• Authors: Shuaiqi Sun, Congde Yuan, Haoqiang Yang, Mengzhuo Guo, Guiying Wei, Jiangbo Tian,
• Abstract summary: Long-term user lifetime value (LTV) prediction is essential for monetization strategy in digital games.<n>Current models often underestimate long-term value or suffer from poor robustness.<n>We propose a novel LTV prediction framework that integrates short-horizon predictions and order-preserving REgression.
• Score: 0.7256915467062311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-value outliers. While existing models typically rely on either short-cycle observations or strong distributional assumptions, such approaches often underestimate long-term value or suffer from poor robustness. To address these issues, we propose SHort-cycle auxiliary with Order-preserving REgression (SHORE), a novel LTV prediction framework that integrates short-horizon predictions (e.g., LTV-15 and LTV-30) as auxiliary tasks to enhance long-cycle targets (e.g., LTV-60). SHORE also introduces a hybrid loss function combining order-preserving multi-class classification and a dynamic Huber loss to mitigate the influence of zero-inflation and outlier payment behavior. Extensive offline and online experiments on real-world datasets demonstrate that SHORE significantly outperforms existing baselines, achieving a 47.91\% relative reduction in prediction error in online deployment. These results highlight SHORE's practical effectiveness and robustness in industrial-scale LTV prediction for digital games.

Related papers

• Extreme value forecasting using relevance-based data augmentation with deep learning models [3.503370263836711]
  In this study, we present a data augmentation framework for extreme value forecasting.<n>We use deep learning models in combination with data augmentation models such as GANs and synthetic minority oversampling technique (SMOTE)<n>Our results indicate that the SMOTE-based strategy consistently demonstrated superior adaptability, leading to improved performance across both short and long-horizon forecasts.
  arXiv  Detail & Related papers  (2025-10-02T06:10:27Z)
• $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting [11.83736650205371]
  Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation.<n>We introduce $K2$VAE, an efficient VAE-based generative model that transforms nonlinear time series into a linear dynamical system.<n>$K2$VAE outperforms state-of-the-art methods in both short- and long-term PTSF, providing a more efficient and accurate solution.
  arXiv  Detail & Related papers  (2025-05-29T02:52:59Z)
• Mini-Game Lifetime Value Prediction in WeChat [20.046082356748663]
  LifeTime Value (LTV) prediction endeavors to forecast the cumulative purchase contribution of a user to a particular item.<n>The purchase rate among registered users is often as critically low as 0.1%, resulting in a dataset where the majority of users make only several purchases.
  arXiv  Detail & Related papers  (2025-05-21T02:28:26Z)
• TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting [49.6208017412376]
  TimeBridge is a novel framework designed to bridge the gap between non-stationarity and dependency modeling.<n>TimeBridge consistently achieves state-of-the-art performance in both short-term and long-term forecasting.
  arXiv  Detail & Related papers  (2024-10-06T10:41:03Z)
• Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals [0.0]
  A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies.
  arXiv  Detail & Related papers  (2024-09-29T11:20:20Z)
• Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty [49.431361908465036]
  We propose a robust model training and evaluation framework to mitigate label variance and extremes. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs. Our approach demonstrates notable improvements over production models, achieving a remarkable textbf17.11% enhancement on offline data.
  arXiv  Detail & Related papers  (2024-04-12T07:47:02Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Inside the black box: Neural network-based real-time prediction of US recessions [0.0]
  Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to model US recessions from 1967 to 2021. Shap method delivers key recession indicators, such as the S&P 500 index for short-term forecasting up to 3 months.
  arXiv  Detail & Related papers  (2023-10-26T16:58:16Z)
• Performative Time-Series Forecasting [64.03865043422597]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective.<n>We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts.<n>We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Forecasting The JSE Top 40 Using Long Short-Term Memory Networks [1.6114012813668934]
  This paper uses a long-short term memory network to perform financial time series forecasting on the return data of the JSE Top 40 index. The paper concludes that the long short-term memory network outperforms the seasonal autoregressive integrated moving average model.
  arXiv  Detail & Related papers  (2021-04-20T09:39:38Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

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.