With the pervasiveness of GPS-enabled devices, a considerable number of GPS traces are accumulating continuously and unobtrusively in online communities. However, almost all current applications directly use raw GPS data, such as coordinates and time stamps, without interpreting these data. Thus far, online communities cannot offer much support to users in terms of recommending geospatial locations. Furthermore, because the data sets involved are large, users cannot browse each GPS trajectory individually. Therefore, users’ GPS trajectories must be mined and then classified as positive or negative. When the number of ratings for a place exceeds a certain threshold, the place is considered suitable for the user. By contrast, when the ratings for a place are mostly negative, this place is considered unsuitable for the user. When a user searches for the best place, the recommender system determines the user’s location (latitude, longitude) and then sends the best-rated destinations and the shortest routes between the user’s location and the destination to the user’s mobile device. Experiments were conducted in this study to determine the requisite similarity for GPS data points, the user’s information, and the best route for the user.

中文翻译：

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使用上下文感知方法改进位置过滤

随着启用GPS的设备的普及，在线社区中不断累积着大量的GPS迹线。但是，几乎所有当前的应用程序都直接使用原始GPS数据，例如坐标和时间戳，而无需解释这些数据。到目前为止，在推荐地理空间位置方面，在线社区无法为用户提供太多支持。此外，由于涉及的数据集很大，因此用户无法单独浏览每个GPS轨迹。因此，必须先确定用户的GPS轨迹，然后将其分类为正或负。当某地点的评分数量超过某个阈值时，该地点被认为适合用户。相反，当某个地点的评分大多为负数时，则认为该地点不适合用户。当用户搜索最佳地点时，推荐系统确定用户的位置（纬度，经度），然后将评分最高的目的地和用户位置与目的地之间的最短路线发送给用户的移动设备。在这项研究中进行了实验，以确定GPS数据点所需的相似性，用户的信息以及用户的最佳路线。