The ubiquitous connectivity of Location-Based Systems (LBS) allows people to share individual location-related data anytime. In this sense, Location-Based Social Networks (LBSN) provides valuable information to be available in large-scale and low-cost fashion via traditional data collection methods. Moreover, this data contains spatial, temporal, and social features of user activity, enabling a system to predict user mobility. In this sense, mobility prediction plays crucial roles in urban planning, traffic forecasting, advertising, and recommendations, and has thus attracted lots of attention in the past decade. In this article, we introduce the Ensemble Random Forest-Markov (ERFM) mobility prediction model, a two-layer ensemble learner approach, in which the base learners are also ensemble learning models. In the inner layer, ERFM considers the Markovian property (memoryless) to build trajectories of different lengths, and the Random Forest algorithm to predict the user’s next location for each trajectory set. In the outer layer, the outputs from the first layer are aggregated based on the classification performance of each weak learner. The experimental results on the real user trajectory dataset highlight a higher accuracy and f1-score of ERFM compared to five state-of-the-art predictors.

中文翻译：

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使用LBSN数据的基于随机森林和马尔可夫性质的集合移动性预测器

基于位置的系统（LBS）的无处不在的连接性使人们可以随时共享与位置相关的数据。从这个意义上说，基于位置的社交网络（LBSN）通过传统的数据收集方法以大规模和低成本的方式提供了有价值的信息。此外，此数据包含用户活动的空间，时间和社交特征，从而使系统能够预测用户的移动性。从这个意义上讲，移动性预测在城市规划，交通预测，广告和推荐中起着至关重要的作用，因此在过去十年中引起了很多关注。在本文中，我们介绍了集成随机森林-马尔可夫（ERFM）移动性预测模型，这是一种两层的集成学习器方法，其中基础学习者也是集成学习模型。在内层 ERFM认为Markovian属性（无内存）可以构建不同长度的轨迹，而Random Forest算法则可以预测每个轨迹集的用户下一个位置。在外层，基于每个弱学习者的分类性能，汇总来自第一层的输出。与五个最新的预测器相比，真实用户轨迹数据集上的实验结果强调了ERFM的更高准确性和f1分数。