构建端到端机器学习系统，预测玩家下一次内购时间。
Development of an End-to-end Machine Learning System with Application to In-app Purchases
- 基于玩家行为数据构建端到端预测模型
- 可精准预判玩家内购时机以提升转化率
- 适合游戏运营与推荐系统开发者参考
机器学习系统在移动游戏行业中日益重要。King等公司已将其应用于优化游戏体验的多个环节。其中关键领域是内购：玩家为增强和定制游戏体验而在游戏中进行的购买。本文描述了我们如何开发一个机器学习系统，用于预测玩家下次内购的时间。这些预测结果被用于向玩家展示优惠活动。文章简要介绍问题定义、建模方法与实验结果，随后详细阐述端到端机器学习系统的实现过程。最后，总结遇到的挑战并展望未来工作方向。
原文摘要 · Abstract (English)
Machine learning (ML) systems have become vital in the mobile gaming industry. Companies like King have been using them in production to optimize various parts of the gaming experience. One important area is in-app purchases: purchases made in the game by players in order to enhance and customize their gameplay experience. In this work we describe how we developed an ML system in order to predict when a player is expected to make their next in-app purchase. These predictions are used to present offers to players. We briefly describe the problem definition, modeling approach and results and then, in considerable detail, outline the end-to-end ML system. We conclude with a reflection on challenges encountered and plans for future work.
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