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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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[1] 章志刚, 金澈清, 王晓玲, 等. 面向海量低质手机轨迹数据的重要位置发现[J]. 软件学报, 2016, 27(7): 1700-1714.
[1] (Zhang Zhigang, Jin Cheqing, Wang Xiaoling, et al.Discovering Important Locations from Massive and Low-Quality Cell Phone Trajectory Data[J]. Journal of Software, 2016, 27(7): 1700-1714.)
[2] Isaacman S, Becker R, Cáceres R, et al.Identifying Important Places in People’s Lives from Cellular Network Data[C]// Proceedings of the 2011 International Conference on Pervasive Computing. 2011: 133-151.
[3] 陈佳, 胡波, 左小清, 等. 利用手机定位数据的用户特征挖掘[J]. 武汉大学学报: 信息科学版, 2014, 39(6): 734-738, 744.
[3] (Chen Jia, Hu Bo, Zuo Xiaoqing, et al.Personal Profile Mining Based on Mobile Phone Location Data[J]. Geomatics & Information Science of Wuhan University, 2014, 39(6): 734-738, 744.)
[4] Bao J, Zheng Y, Mokbel M F.Location-Based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data[C]// Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012: 199-208.
[5] 文长江. 基于社交数据用户行为的时空特性分析[D]. 成都: 电子科技大学, 2018.
[5] (Wen Changjiang.Analysis of Spatio-temporal Characteristics of User Behavior Based on Social Data[D]. Chengdu: University of Electronic Science and Technology of China, 2018.)
[6] Ashbrook D, Starner T.Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users[J]. Personal & Ubiquitous Computing, 2003, 7(5): 275-286.
[7] 丰江帆, 熊雨虹. 一种基于个人位置信息的重要地点识别方法[J]. 小型微型计算机系统, 2013, 34(3): 503-507.
[7] (Feng Jiangfan, Xiong Yuhong.An Important Place Identification Algorithm Based on Personal GPS Location[J]. Journal of Chinese Computer Systems, 2013, 34(3): 503-507.)
[8] Cho E, Myers S A, Leskovec J.Friendship and Mobility: User Movement in Location-based Social Networks[C]// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011: 1082-1090.
[9] Ma C L, Ma T, Shan H.A New Important-Place Identification Method[C]// Proceedings of the 2015 IEEE International Conference on Computer & Communications. IEEE, 2016: 151-155.
[10] Mikolov T, Sutskever I, Chen K, et al.Distributed Representations of Words and Phrases and Their Compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 3111-3119.
[11] Kiros R, Zhu Y, Salakhutdinov R, et al.Skip-Thought Vectors[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015: 3294-3302.
[12] Le Q, Mikolov T.Distributed Representations of Sentences and Documents[C]// Proceedings of the 31st International Conference on Machine Learning. 2014: 1188-1196.
[13] Liu Y, Liu Z, Chua T S, et al.Topical Word Embeddings[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 2418-2424.
[14] Grover A, Leskovec J.Node2Vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2016: 855-864.
[15] Feng S, Cong G, An B, et al.POI2Vec: Geographical Latent Representation for Predicting Future Visitors[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 102-108.
[16] Sadilek A, Kautz H, Bigham J P.Finding Your Friends and Following Them to Where You are[C]// Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012: 723-732.
[17] Freeman L C.Centrality in Social Networks Conceptual Clarification[J]. Social Networks, 1978, 1(3): 215-239.
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