TraDR:一种基于轨迹分解重构的移动社交网络位置预测方法

TraDR:A Destination Prediction Method Based on Trajectory Decomposition and Reconstruction in Geo-social Networks

随着移动社交网络的不断发展,利用用户发布的位置信息为其提供基于地域的个性化推荐服务不仅给用户提供了便利,也为商户带来了巨大的潜在利益。位置预测技术作为此类服务中的关键技术,是移动社交网络中的重要研究内容之一。结合移动社交网络的特点,提出了基于轨迹"分解-重构"的位置预测方法 TraDR,利用公开易得的先验知识,为用户建立个性化的位置推理模型,有效解决了常见位置预测算法所面临的"轨迹数据稀疏问题"。基于真实数据集的实验验证了该预测方法在预测有效性及效率方面的优越性。

With the development of geo-social networks,the practice of utilizing the locations published by GeoSN users to offer them personalized reference services not only benefits users,but also brings the business providers potential profits.As the fundamental enabling technology of the location-based reference services,destination prediction becomes one of the most significant research topics in GeoSNs.Considering the features of GeoSNs,this paper proposed a novel destination prediction method named TraDR specially for GeoSNs based on trajectory decomposition and reconstruction to construct personalized inference model for each target GeoSN user,which not only solves the ＂trajectory data sparsity problem＂ faced by common location inference models,but also takes advantages of the rich commonly available public background information.Experiments based on real world dataset were carried out,and results prove the high performance of the presented method both in prediction accuracy and running efficiency.