使用上下文树预测交互和上下文

Alasdair Thomason, N. Griffiths, Victor Sanchez
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引用次数: 2

摘要

根据地理空间数据预测个人未来的行动,有可能为量身定制的服务提供基础。本文提出了预测上下文树(PCT),一种基于上下文树摘要模型[8]的新的分层分类器。PCT能够预测个人未来的环境和位置,不仅为了解用户将在哪里,而且为了解他们将从事何种活动提供基础。通过与现有技术的比较,本文展示了PCT的适用性,显示了PCT在位置预测方面的准确性提高,并通过上下文预测提高了实用性。
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Predicting interactions and contexts with context trees
Predicting the future actions of individuals from geospatial data has the potential to provide a basis for tailored services. This work presents the Predictive Context Tree (PCT), a new hierarchical classifier based on the Context Tree summary model [8]. The PCT is capable of predicting the future contexts and locations of individuals to provide a basis for understanding not only where a user will be, but also what type of activity they will be performing. Through a comparison to established techniques, this paper demonstrates the applicability of the PCT by showing increased accuracies for location prediction, and increased utility through context prediction.
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