English(EN) User Representation via Cross Multi-source Behavior Pre-training for Mobile Games

新CM-PTM模型通过跨源行为分析增强移动游戏个性化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新颖的跨多源行为预训练模型（CM-PTM），以改进移动游戏个性化的用户表示。该模型通过考虑移动设备上用户活动的复杂性、跨源性和多粒度性，解决了现有方法的局限性。CM-PTM利用分层级联的掩码预测代理任务，统一了不同行为源和细粒度动态之间的依赖性建模，从而在下游移动游戏推荐任务中取得了显著的性能提升。 AI

影响这项研究通过更好地理解玩家在各种来源上的行为，有望在移动游戏中带来更有效和个性化的用户体验。

排序理由该集群包含一篇详细介绍移动游戏用户表示新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

arXiv
CM-PTM

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 30 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍移动游戏用户表示新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chengqi Yang, Yiran Qiao, Feng Liu, Xingyu Lou, Zijun Zhou, Xiaoyun Mo, Changwang Zhang, Jiayuan Xu, Jun Wang, Xiang Ao · 2026-09-02 04:00

面向手机游戏的跨多源行为预训练用户表示

arXiv:2609.01057v1 Announce Type: new 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 th…

本文如何被排名

报道来源 [1]

面向手机游戏的跨多源行为预训练用户表示

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