Journal of Computer Science and Technology ›› 2018, Vol. 33 ›› Issue (4): 697-710.doi: 10.1007/s11390-018-1850-3

Special Issue: 3; Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Recommender Systems with Big Data • Previous Articles     Next Articles

Discovering Functional Organized Point of Interest Groups for Spatial Keyword Recommendation

Yan-Xia Xu, Wei Chen, Jia-Jie Xu, Member, CCF, ACM, Zhi-Xu Li, Member, CCF, ACM Guan-Feng Liu, Member, CCF, ACM, Lei Zhao*, Member, CCF, ACM   

  1. School of Computer Science and Technology, Soochow University, Suzhou 215006, China
  • Received:2017-12-27 Revised:2018-05-07 Online:2018-07-05 Published:2018-07-05
  • Contact: Lei Zhao,E-mail:zhaol@suda.edu.cn E-mail:zhaol@suda.edu.cn
  • About author:Yan-Xia Xu received her B.S. degree in computer science from Soochow University, Suzhou, in 2015. She is currently a M.S. candidate in Soochow University, Suzhou. Her research interests include data mining, spatial-temporal database and graph computing.
  • Supported by:

    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.

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.

Key words: functional organized point of interest (POI) group; POI clustering; OPG-LDA (organized point of interest group-latent Dirichlet allocation) model; spatial keyword recommendation;

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