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 \textbf{17.11}\% enhancement on offline data and an
impressive \textbf{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.

通过提出一个稳健的模型训练和评估框架，以标准化支出数据以降低标签变异性和异常值，并确保建模过程的稳定性，该论文介绍了一种协同增强模型，旨在预测用户游戏消费而无需依赖用户标识，从而确保用户隐私并实现在线训练，通过在融合用户偏好和游戏特征之前分别表示它们并将其作为支出预测模块的输入，通过严格的实验，证明了我们的方法在离线数据上实现了显著的 17.11% 的改进，并在在线 A/B 测试中获得了令人瞩目的 50.65% 的提升，总结我们的贡献突显了稳定的模型训练框架的重要性以及协同增强模型在移动游戏中预测用户消费行为方面的功效。

消费不确定性下新下载移动游戏花费的协同增强预测

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

Mobile gaming has emerged as a promising market with billion-dollar revenues.
A variety of mobile game platforms and services have been developed around the
world. One critical challenge for these platforms and services is to understand
user churn behavior in mobile games. Accurate churn prediction will benefit
many stakeholders such as game developers, advertisers, and platform operators.
In this paper, we present the first large-scale churn prediction solution for
mobile games. In view of the common limitations of the state-of-the-art methods
built upon traditional machine learning models, we devise a novel
semi-supervised and inductive embedding model that jointly learns the
prediction function and the embedding function for user-app relationships. We
model these two functions by deep neural networks with a unique edge embedding
technique that is able to capture both contextual information and relationship
dynamics. We also design a novel attributed random walk technique that takes
into consideration both topological adjacency and attribute similarities. To
evaluate the performance of our solution, we collect real-world data from the
Samsung Game Launcher platform that includes tens of thousands of games and
hundreds of millions of user-app interactions. The experimental results with
this data demonstrate the superiority of our proposed model against existing
state-of-the-art methods.

该论文提出了一种大规模的流失预测解决方案，使用经过半监督和归纳嵌入模型的深度神经网络以及独特的边嵌入技术来捕捉上下文信息和关系动态，同时考虑拓扑邻接和属性相似度， 以预测用户在移动游戏中的流失行为，并在三星游戏启动器平台上的真实世界数据上证明了其优越性。