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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (6): 75-82    DOI: 10.11925/infotech.2096-3467.2018.1085
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Discovering Important Locations with User Representation and Trace Data
Qingtian Zeng1,2,Mingdi Dai2,Chao Li1,3(),Hua Duan2,Zhongying Zhao2
1(College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China)
2(College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)
3(Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China)
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[Objective] This paper tries to discover the important locations of users, aiming to provide good data support for user behavior studies. [Methods] We presented a model for predicting important locations based on user representation. First, we proposed a vectorized representation method to predict user behaviors based on Word2Vec. Then, we constructed a user relationship network based on the similarity of user vectors to extract core users. Finally, we predicted the important locations by the behaviors of core users. [Results] The precison of important locations classifiction was 7% higher than those of the exisitng methods. Moreover, the residential and commercial areas were shown in the labeled map. [Limitations] Our method can only identify residential and business areas. [Conclusions] The proposed method could effectively find important locations and provide more supports to study user behaviors.

Key wordsImportant Locations      Trajectory Mining      Representation Learning      Support Vector Machine     
Received: 28 September 2018      Published: 15 August 2019

Cite this article:

Qingtian Zeng,Mingdi Dai,Chao Li,Hua Duan,Zhongying Zhao. Discovering Important Locations with User Representation and Trace Data. Data Analysis and Knowledge Discovery, 2019, 3(6): 75-82.

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