中文摘要：

下地点预言为许多基于地点的应用程序是很重要的。与稳固的理论基础的优点，基于 Markov 的途径沿着这个方向获得了成功。在这份报纸，我们寻求由理解目标的类似提高预言表演。特别地，我们建议一个新奇方法，叫的加权的 Markov 模型(加权 -- 公里) ，它两个都利用在采矿的公平通行证的地点和目标类似的顺序活动性模式。到这个目的，我们首先与它的自己的轨道记录为每个目标训练一个 Markov 模型，然后确定从二个方面的不同目标的类似：空间地区类似和轨道类似。最后，我们由把类似看作到达下次可能的各个的概率的重量把目标类似合并到 Markov 模型地点，和回来是的 top-rankings 结果。我们在真实数据集上进行了广泛的实验，并且结果在存在解决方案上在预言精确性表明重要改进。

英文摘要：

Next location prediction is of great importance for many location-based applications. With the virtue of solid theoretical foundations, Markov-based approaches have gained success along this direction. In this paper, we seek to enhance the prediction performance by understanding the similarity between objects. In particular, we propose a novel method, called weighted Markov model （weighted-MM）, which exploits both the sequence of just-passed locations and the object similarity in mining the mobility patterns. To this end, we first train a Markov model for each object with its own trajectory records, and then quantify the similarities between different objects from two aspects： spatial locality similarity and trajectory similarity. Finally, we incorporate the object similarity into the Markov model by considering the similarity as the weight of the probability of reaching each possible next location, and return the top-rankings as results. We have conducted extensive experiments on a real dataset, and the results demonstrate significant improvements in prediction accuracy over existing solutions.