We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, ...We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search(DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.展开更多
A point of interest （POI） is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups （FOPGs）, such as shopping malls, ...A point of interest （POI） is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups （FOPGs）, such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people＇s lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups （OPGs） and utilize OPGs-LDA （Latent Dirichlet Allocation） model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.展开更多
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LZ13F020001)the National Natural Science Foundation of China(Nos.61173185 and 61173186)+1 种基金the National Key Technology R&D Program of China(No.2012BAI34B01)the Hangzhou S&T Development Plan(No.20150834M22)
文摘We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search(DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335, 61472263, 61402312 and 61402313, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.
文摘A point of interest （POI） is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups （FOPGs）, such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people＇s lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups （OPGs） and utilize OPGs-LDA （Latent Dirichlet Allocation） model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.
