Problem
Research questions and friction points this paper is trying to address.
Cross Multi-source Behavior
User Representation Pre-training
Mobile Games
Device-level Behavioral Logs
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
🤖 AI Summary
该研究针对移动设备上跨源多粒度用户行为的表示学习问题，提出了一种新的预训练模型CM-PTM，通过层次级联掩码预测任务来解决数据稀疏性问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross Multi-source Behavior
Hierarchical Cascaded Mask-then-predict
Unified Modeling
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