Mining geographic-temporal-semantic patterns in trajectories for location prediction

Josh Jia Ching Ying, Wang Chien Lee, S. Tseng*

*Corresponding author for this work

研究成果: Article同行評審

97 引文 斯高帕斯(Scopus)


In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.

期刊ACM Transactions on Intelligent Systems and Technology
出版狀態Published - 1 十二月 2013

指紋 深入研究「Mining geographic-temporal-semantic patterns in trajectories for location prediction」主題。共同形成了獨特的指紋。