面向移动游戏的跨多源行为预训练用户表示
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
- Institute of Computing Technology, Chinese Academy of Sciences (CAS)（中国科学院计算技术研究所）
- University of Chinese Academy of Sciences, CAS（中国科学院大学）
- OPPO Research Institute（OPPO研究院）
- Institute of Intelligent Computing Technology, CAS（中国科学院智能计算技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有用户表示预训练忽略移动设备用户跨源多粒度行为的问题，提出CM-PTM模型，通过分层级联的掩码预测任务建模跨源依赖，在移动游戏推荐任务中实现显著性能提升。
AI中文摘要：
用户表示预训练已成为缓解下游个性化任务中数据稀疏问题的基础范式。然而现有研究主要关注单应用或应用级行为，忽略了移动设备上用户活动固有的跨源和多粒度特性。在设备层面，用户意图产生于异构行为源与分层动作结构之间的复杂交互，这是传统以应用为中心的建模无法解决的挑战。为解决该问题，我们提出CM-PTM，一种专为基于设备级行为日志的移动游戏用户表示学习设计的新型跨多源行为预训练模型。CM-PTM采用分层级联的“先掩码后预测”代理任务，首先推断下一个行为的来源，然后在应用-动作级别逐步细化预测。该设计能在单一预训练范式内统一建模跨源依赖与细粒度行为动态。在大规模真实移动数据集上的大量实验表明，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持续带来显著性能提升。
英文摘要：
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.
