# Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection

arXiv: https://arxiv.org/abs/2308.12729 · HTML: https://mlsanity.com/paper/2308.12729/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (2020)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou](https://mlsanity.com/paper/2208.13358/) (2022)
- [Customer Lifetime Value Prediction Using Embeddings](https://mlsanity.com/paper/1703.02596/) (2017)
- [Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models](https://mlsanity.com/paper/1811.12799/) (2018)
- [Thinking inside The Box: Learning Hypercube Representations for Group Recommendation](https://mlsanity.com/paper/2204.02592/) (2022)

## Built on by (most cited first, 11 of 11)

- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty](https://mlsanity.com/paper/2404.08301/) (2024)
- [ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://mlsanity.com/paper/2406.10517/) (2024)
- [Mini-Game Lifetime Value Prediction in WeChat](https://mlsanity.com/paper/2506.11037/) (2025)
- [CC-OR-Net: A Unified Framework for LTV Prediction through Structural Decoupling](https://mlsanity.com/paper/2601.10176/) (2026)
- [Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout](https://mlsanity.com/paper/2411.15944/) (2024)
- [LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategy](https://mlsanity.com/paper/2411.16095/) (2024)
- [A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation](https://mlsanity.com/paper/2503.03165/) (2025)
- [SILS: Strategic Influence on Liquidity Stability and Whale Detection in Concentrated-Liquidity DEXs](https://mlsanity.com/paper/2507.19411/) (2025)
- [HT-GNN: Hyper-Temporal Graph Neural Network for Customer Lifetime Value Prediction in Baidu Ads](https://mlsanity.com/paper/2601.13013/) (2026)
- [AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction](https://mlsanity.com/paper/2602.21634/) (2026)

## Abstract (arXiv)

Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets.