预测智能家居中用户下一步操作的时间，让系统更主动高效。
Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes
- 用Transformer编码器建模动作时间序列，捕捉时序依赖关系。
- 在合成数据集上准确率达38.30%，比最优基线提升6%。
- 适用于需预判用户行为的智能助手与自动化系统研发者。
物联网设备的普及产生了大量交互数据，为理解用户行为提供了可能。尽管已有研究关注用户将执行何种操作，但操作发生的时间——对实现主动、高效的智能系统至关重要——仍相对缺乏探索。为此，本文聚焦于预测智能环境中用户下一次操作的时间。由于缺乏适合该任务的公开细粒度时间戳数据集及隐私顾虑，我们基于人类对交互模式的标注，合成了一组包含11.6k条序列的数据集，并为每个动作配以精确时间戳。为此，我们提出Timing-Matters方法，采用Transformer-Encoder架构预测动作时间，在合成数据集上达到38.30%的准确率，优于最佳基线6%，并在其他公开数据集上实现1–6%的提升。相关代码与数据集将公开发布。
原文摘要 · Abstract (English)
The proliferation of IoT devices generates vast interaction data, offering insights into user behaviour. While prior work predicts what actions users perform, the timing of these actions -- critical for enabling proactive and efficient smart systems -- remains relatively underexplored. Addressing this gap, we focus on predicting the time of the next user action in smart environments. Due to the lack of public datasets with fine-grained timestamps suitable for this task and associated privacy concerns, we contribute a dataset of 11.6k sequences synthesized based on human annotations of interaction patterns, pairing actions with precise timestamps. To this end, we introduce Timing-Matters, a Transformer-Encoder based method that predicts action timing, achieving 38.30% accuracy on the synthesized dataset, outperforming the best baseline by 6%, and showing 1--6% improvements on other open datasets. Our code and dataset will be publicly released.
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