用时间门控增强Transformer，精准预测手机应用使用行为
TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction
- 引入时间门控模块，按时段动态调节特征维度
- 在两个真实数据集上超越15种基线模型，冷启动场景表现优
- 可解释每日使用规律，适合需理解用户习惯的研究
准确预测智能手机应用使用行为面临用户行为稀疏和不规则的挑战，尤其在冷启动和低活跃度情况下。现有方法多依赖静态或仅注意力架构，难以捕捉细粒度时间动态。本文提出TGT，一种配备时间门控模块的Transformer框架，该模块基于小时信息条件化隐藏表示。与传统时间嵌入不同，时间门控以时间感知方式自适应地重缩放特征维度，与自注意力机制正交，增强时间敏感性。TGT还引入上下文感知编码器，将会话序列与用户画像融合为统一表征。在两个真实数据集（Tsinghua App Usage 和 LSApp）上的实验表明，TGT显著优于15种竞争基线，在HR@1指标上取得明显提升，并在冷启动场景保持鲁棒性。对门控向量的分析揭示了可解释的日常使用节奏，表明TGT学习到符合人类规律的应用行为模式。这些结果确立TGT为一种强大且可解释的时间感知应用使用预测框架。
原文摘要 · Abstract (English)
Accurately predicting smartphone app usage is challenging due to the sparsity and irregularity of user behavior, especially under cold-start and low-activity conditions. Existing approaches mostly rely on static or attention-only architectures, which struggle to model fine-grained temporal dynamics. We propose TGT, a Transformer framework equipped with a temporal gating module that conditions hidden representations on the hour-of-day. Unlike conventional time embeddings, temporal gating adaptively rescales feature dimensions in a time-aware manner, working orthogonally to self-attention and strengthening temporal sensitivity. TGT further incorporates a context-aware encoder that integrates session sequences and user profiles into a unified representation. Experiments on two real-world datasets, Tsinghua App Usage and LSApp, demonstrate that TGT significantly outperforms 15 competitive baselines, achieving notable gains in HR@1 and maintaining robustness under cold-start scenarios. Beyond accuracy, analysis of gating vectors uncovers interpretable daily usage rhythms, showing that TGT learns human-consistent patterns of app behavior. These results establish TGT as both a powerful and interpretable framework for time-aware app usage prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。