# Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty

arXiv: https://arxiv.org/abs/2404.08301 · HTML: https://mlsanity.com/paper/2404.08301/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://mlsanity.com/paper/2001.10167/) (2020)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://mlsanity.com/paper/2109.12613/) (2021)
- [DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation](https://mlsanity.com/paper/2002.00844/) (2020)
- [Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling](https://mlsanity.com/paper/2106.01040/) (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)
- [Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection](https://mlsanity.com/paper/2308.12729/) (2023)
- [Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction](https://mlsanity.com/paper/2307.12756/) (2023)
- [Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2402.11523/) (2024)

## Built on by (most cited first, 7 of 7)

- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Large Language Models as Evaluators for Recommendation Explanations](https://mlsanity.com/paper/2406.03248/) (2024)
- [Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://mlsanity.com/paper/2405.20718/) (2024)
- [RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors](https://mlsanity.com/paper/2601.08705/) (2026)
- [When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation](https://mlsanity.com/paper/2409.12730/) (2024)
- [Denoised Recommendation Model with Collaborative Signal Decoupling](https://mlsanity.com/paper/2511.04237/) (2025)
- [Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrix](https://mlsanity.com/paper/2605.20721/) (2026)

## Abstract (arXiv)

With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending data to mitigate label variance and extremes, ensuring stability in the modeling process. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs, thus ensuring user privacy and enabling seamless online training. Our model adopts a unique approach by separately representing user preferences and game features before merging them as input to the spending prediction module. Through rigorous experimentation, our approach demonstrates notable improvements over production models, achieving a remarkable 17.11% enhancement on offline data and an impressive 50.65% boost in an online A/B test. In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.