Abstract
The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.
Highlights
Recent years have witnessed the rapid development of Internet technologies and sensor devices, which, in turn, has resulted in the explosive growth of geo-related information
With the grids viewed as the candidate ROIs, the ROI vectors were obtained by their internal POI vectors, which implies the type characteristics and spatial distribution of each candidate ROI
Considering that some infrequent POIs tend to have a negative impact on the function and type of the ROI, term frequency-inverse document frequency (TF-IDF), a common method in information retrieval that can evaluate the importance of words in a corpus, was utilized to adjust the weights of POI types in each ROI
Summary
Recent years have witnessed the rapid development of Internet technologies and sensor devices, which, in turn, has resulted in the explosive growth of geo-related information. The existing ROI exploration methods are mainly based on the statistical information or the density information of the query elements [6,7,8,9], such as POIs with certain keywords [10,11], while neglecting the influence of internal and environmental characteristics of the region. In order to achieve an efficient query of the relevant ROI, we executed a grid division on the whole research region so that each grid contained a certain number of POIs. With the grids viewed as the candidate ROIs, the ROI vectors were obtained by their internal POI vectors, which implies the type characteristics and spatial distribution of each candidate ROI.
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