Discovering Functional Organized Keyword Recommendation Point of Interest Groups for Spatial
Discovering Functional Organized Keyword Recommendation Point of Interest Groups for Spatial作者机构：School of Computer Science and Technology Soochow University Suzhou 215006 China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2018年第33卷第4期
页 面：697-710页
核心收录：
学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
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
主 题：functional organized point of interest (POI) group POI clustering OPG-LDA (organized point of interest group-latent Dirichlet allocation) model spatial keyword recommendation
摘 要：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.
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