CM-PTM模型解决了移动设备用户行为建模的跨源挑战，能更好捕捉用户兴趣，提升游戏推荐效果。
CM-PTM是一种新型跨多源行为预训练模型，专为移动游戏用户表示学习设计。该模型采用分层级联掩码预测任务，首先预测下一个行为的来源，然后在应用-操作级别逐步细化预测。在真实大规模移动数据集上的实验表明，CM-PTM能有效捕捉用户的内生兴趣，并在下游移动游戏推荐任务中取得显著性能提升。
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus on single-app or app-level behaviors, overlooking the inherently cross-source and multi-granular nature of user activities on mobile devices. At the device level, user intent emerges from complex interactions among heterogeneous behavior sources and hierarchical action structures, posing challenges that cannot be addressed by conventional app-centric modeling. To tackle this issue, we propose CM-PTM, a novel Cross Multi-source Behavior Pre-Training Model tailored for mobile game user representation learning on device-level behavioral logs. CM-PTM employs hierarchical cascaded mask-then-predict proxy tasks that first infer the source of the next behavior and then progressively refine predictions at the app-action level. This design enables unified modeling of cross-source dependencies and fine-grained behavioral dynamics within a single pre-training paradigm. Extensive experiments on large-scale real-world mobile datasets demonstrate that CM-PTM effectively captures users' endogenous interests and consistently delivers significant performance gains on downstream mobile game recommendation tasks.