Improved location filtering using a context-aware approach

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.