计算机应用 ›› 2016, Vol. 36 ›› Issue (1): 33-38.DOI: 10.11772/j.issn.1001-9081.2016.01.0033
• 第32届中国数据库学术会议(NDBC 2015) • 上一篇 下一篇
收稿日期:
2015-07-10
修回日期:
2015-08-04
出版日期:
2016-01-10
发布日期:
2016-01-09
通讯作者:
刘洋(1977-),女,山东济南人,副教授,博士,CCF会员,主要研究方向:情感分析、文本挖掘
作者简介:
陈勐(1990-),男,山东滕州人,博士研究生,主要研究方向:轨迹挖掘、城市计算;禹晓辉(1977-),男,山东济南人,教授,博士生导师,博士,CCF会员,主要研究方向:大数据管理、数据挖掘。
基金资助:
CHEN Meng, YU Xiaohui, LIU Yang
Received:
2015-07-10
Revised:
2015-08-04
Online:
2016-01-10
Published:
2016-01-09
Supported by:
摘要: 针对时空轨迹中位置顺序和时间对于理解用户移动模式的重要性,提出了一种新的用户轨迹深度表示模型。该模型考虑到时空轨迹的特点:1)不同的位置顺序表示不同的移动模式;2)轨迹有周期性并且在不同的时间段有变化。首先,将两个连续的位置点组合成位置序列;然后,将位置序列和对应的时间块组合成时间位置序列,作为描述轨迹特征的基本单位;最后,利用深度表示模型为每个序列训练特征向量。为了验证深度表示模型的有效性,设计实验将时间位置序列向量应用到用户移动模式发现中,并利用Gowalla签到数据集进行了实验评测。实验结果显示提出的模型能够发现"上班""购物"等明确的模式,而Word2Vec很难发现有意义的移动模式。
中图分类号:
陈勐, 禹晓辉, 刘洋. 基于深度表示模型的移动模式挖掘[J]. 计算机应用, 2016, 36(1): 33-38.
CHEN Meng, YU Xiaohui, LIU Yang. Mining mobility patterns based on deep representation model[J]. Journal of Computer Applications, 2016, 36(1): 33-38.
[1] COOK D. How smart is your home? [J]. Science, 2012, 335(6076): 1579-1581. [2] GRADY M, HARE G. How smart is your city? [J]. Science, 2012, 335(6076): 1581-1582. [3] CHEN Z, SHEN H, ZHOU X. Discovering popular routes from trajectories [C]// ICDE 2011: Proceedings of the 27th International Conference on Data Engineering. Piscataway, NJ: IEEE, 2011: 900-911. [4] YUAN N, ZHENG Y, ZHANG L, et al. T-Finder: a recommender system for finding passengers and vacant taxis [J]. IEEE transactions on knowledge and data engineering, 2013, 25(10): 2390-2403. [5] ZHENG Y, LIU Y, YUAN J, et al. Urban computing with taxicabs [C]// UbiComp 2011: Proceedings of the 13th International Conference on Ubiquitous Computing. New York: ACM, 2011: 89-98. [6] BAO J, ZHENG Y, MOKBEL M. Location-based and preference-aware recommendation using sparse geo-social networking data [C]// GIS 2012: Proceedings of the 20th International Conference on Advances in Geographic Information Systems. New York: ACM, 2012: 199-208. [7] SON J, KIM A, PARK S. A location-based news article recommendation with explicit localized semantic analysis [C]// SIGIR 2013: Proceedings of the 36th International Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 293-302. [8] HSIEH H, LI C, LIN S. Exploiting large-scale check-in data to recommend time-sensitive routes [C]// UrbComp 2012: Proceedings of the ACM SIGKDD International Workshop on Urban Computing. New York: ACM, 2012: 55-62. [9] KURASHIMA T, IWATA T, HOSHIDE T, et al. Geo topic model: joint modeling of user's activity area and interests for location recommendation [C]// WSDM 2013: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. New York: ACM, 2013: 375-384. [10] YIN Z, CAO L, HAN J, et al. Diversified trajectory pattern ranking in geo-tagged social media [C]// SDM 2011: Proceedings of the SIAM International Conference on Data Mining. Berlin: Springer, 2011: 980-991. [11] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space [EB/OL]. [2015-04-20]. http://arxiv.org/pdf/1301.3781v3.pdf. [12] CAO Z, LI S, LIU Y, et al. A novel neural topic model and its supervised extension [C]// AAAI 2015: Proceedings of the 29th AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2015: 2210-2216. [13] CHEN M, LIU Y, YU X. NLPMM: a next location predictor with Markov modeling [C]// PAKDD 2014: Proceedings of the 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2014: 186-197. [14] FARRAHI K, GATICA-PEREZ D. Discovering routines from large-scale human locations using probabilistic topic models [J]. ACM transactions on intelligent systems and technology, 2011, 2(1): Article No. 3. [15] YU Z, FENG Y, XU H, et al. Recommending travel packages based on mobile crowdsourced data [J]. IEEE communications magazine, 2014, 52(8): 56-62. [16] YUAN J, ZHENG Y, ZHANG L, et al. Where to find my next passenger [C]// UbiComp 2011: Proceedings of the 13th International Conference on Ubiquitous Computing. New York: ACM, 2011: 109-118. [17] PANG L, CHAWLA S, LIU W, et al. On mining anomalous patterns in road traffic streams [C]// ADMA 2011: Proceedings of the 7th International Conference on Advanced Data Mining and Applications. Berlin: Springer, 2011: 237-251. [18] PAN G, QI G, WU Z, et al. Land-use classification using taxi GPS traces [J]. IEEE transactions on intelligent transportation systems, 2013, 14(1): 113-123. [19] LI X, PAN G, WU Z, et al. Prediction of urban human mobility using large-scale taxi traces and its applications [J]. Frontiers of computer science, 2012, 6(1): 111-121. [20] CASTRO P, ZHANG D, CHEN C, et al. From taxi GPS traces to social and community dynamics: a survey [J]. ACM computing surveys, 2013, 46(2): Article No. 17. [21] HASAN S, ZHAN X, UKKUSURI S. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media [C]// UrbComp 2013: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. New York: ACM, 2013: Article No. 6. [22] XUE G, LI Z, ZHU H, et al. Traffic-known urban vehicular route prediction based on partial mobility patterns [C]// ICPADS 2009: Proceedings of the 15th International Conference on Parallel and Distributed Systems. Piscataway, NJ: IEEE, 2009: 369-375. [23] MONREALE A, PINELLI F, TRAARTI R, et al. WhereNext: a location predictor on trajectory pattern mining [C]// SIGKDD 2009: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009: 637-646. [24] BLEI D, NG A, JORDAN M. Latent Dirichlet allocation [J]. Journal of machine learning research, 2003, 3: 993-1022. [25] CHANG J, SUN E. Location 3: how users share and respond to location-based data on social networking sites [C]// AAAI 2011: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Menlo Park, CA: AAAI Press, 2011: 74-80. [26] CRANSHAW J, YANO T. Seeing a home away from the home: distilling proto-neighborhoods from incidental data with latent topic modeling [C]// NIPS 2010: Proceedings of the Twenty-Fourth Annual Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates, Inc., 2010: 10. [27] YUAN J, ZHENG Y, XIE X. Discovering regions of different functions in a city using human mobility and POIs [C]// SIGKDD 2012: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012: 186-194. |
[1] | 赵全, 汤小春, 朱紫钰, 毛安琪, 李战怀. 大规模短时间任务的低延迟集群调度框架[J]. 计算机应用, 2021, 41(8): 2396-2405. |
[2] | 冯钧 王秉发 陆佳民. 分布式资源描述框架数据管理系统查询性能评价[J]. 计算机应用, 0, (): 0-0. |
[3] | 李国荣, 冶继民, 甄远婷. 基于新的鲁棒相似性度量的时间序列聚类[J]. 计算机应用, 2021, 41(5): 1343-1347. |
[4] | 林定康 颜嘉麒 巴·楠登 符朕皓 姜皓晨. 门罗币匿名及追踪技术综述[J]. 计算机应用, 0, (): 0-0. |
[5] | 沈忱, 邰凌翔, 彭煜玮. 面向自动参数调优的动态负载匹配方法[J]. 计算机应用, 2021, 41(3): 657-661. |
[6] | 杨程, 陆佳民, 冯钧. 分布式环境下大规模资源描述框架数据划分方法综述[J]. 计算机应用, 2020, 40(11): 3184-3191. |
[7] | 兰海, 韩珂, 申砾, 崔秋, 彭煜玮. TiDB的多索引访问优化[J]. 计算机应用, 2020, 40(2): 410-415. |
[8] | 崔艺馨, 陈晓东. Spark框架优化的大规模谱聚类并行算法[J]. 计算机应用, 2020, 40(1): 168-172. |
[9] | 万静, 郑龙君, 何云斌, 李松. 高维不确定数据的子空间聚类算法[J]. 计算机应用, 2019, 39(11): 3280-3287. |
[10] | 李博, 张晓, 颜靖艺, 李可威, 李恒, 凌玉龙, 张勇. 基于值差度量和聚类优化的K最近邻算法在银行客户行为预测中的应用[J]. 计算机应用, 2019, 39(9): 2784-2788. |
[11] | 李耘书, 滕飞, 李天瑞. 基于微操作的Hadoop参数自动调优方法[J]. 计算机应用, 2019, 39(6): 1589-1594. |
[12] | 霍峥, 张坤, 贺萍, 武彦斌. 满足本地化差分隐私的众包位置数据采集[J]. 计算机应用, 2019, 39(3): 763-768. |
[13] | 朱跃龙, 朱晓晓, 王继民. 基于子序列全连接和最大团的时间序列模体发现算法[J]. 计算机应用, 2019, 39(2): 414-420. |
[14] | 尹远, 张昌, 文凯, 郑云俊. 基于DiffNodeset结构的最大频繁项集挖掘算法[J]. 计算机应用, 2018, 38(12): 3438-3443. |
[15] | 曲立平, 吴家喜. 基于评分可靠性的跨域个性化推荐方法[J]. 计算机应用, 2018, 38(11): 3081-3083. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||