Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of ...Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of the device, we need to reduce it in order to obtain higher positioning accuracy. In this paper, we proposed a new centralized D2D(Device-to-Device) co-location algorithm. This algorithm uses DBSACN(Density-Based Spatial Clustering of Applications with Noise) clustering to reduce the deviation of device location information. Numerical results show that the positioning accuracy of the centralized D2D co-localization algorithm is improved by 62.7% compared with the SPAWN algorithm, which positioning performance superior to the traditional co-localization algorithm.展开更多
位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴...位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。展开更多
基金financially supported by the National Key Research&Development Program under Grant No.2018YFC0809702。
文摘Nowadays, most positioning systems carry out locational calculation based on the accurate location information of some devices in the network. However there is a deviation in the locational information of the part of the device, we need to reduce it in order to obtain higher positioning accuracy. In this paper, we proposed a new centralized D2D(Device-to-Device) co-location algorithm. This algorithm uses DBSACN(Density-Based Spatial Clustering of Applications with Noise) clustering to reduce the deviation of device location information. Numerical results show that the positioning accuracy of the centralized D2D co-localization algorithm is improved by 62.7% compared with the SPAWN algorithm, which positioning performance superior to the traditional co-localization algorithm.
文摘位置社交网络(Location Based Social Network,LBSN)的发展,为兴趣点推荐提供丰富的数据资源。基于地理影响的推荐算法是兴趣点推荐的热门研究话题,而现有的推荐算法缺乏对用户个性化行为的分析。因此,提出一种基于用户空间相似性的兴趣点推荐算法。首先,利用用户签到数据构建空间分布相似性模型;其次,引入削减因子,提高具有相同签到记录的用户权重;最后,线性融合用户及空间分布性相似性模型对Top-N兴趣点进行推荐,并进行实验验证。实验结果表明,该算法有效提高了兴趣点推荐的质量。
