ABSTRACT
Learning explicit and implicit patterns in human trajectories plays an important role in many Location-Based Social Networks (LBSNs) applications, such as trajectory classification (e.g., walking, driving, etc.), trajectory-user linking, friend recommendation, etc. A particular problem that has attracted much attention recently - and is the focus of our work - is the Trajectory-based Social Circle Inference (TSCI), aiming at inferring user social circles (mainly social friendship) based on motion trajectories and without any explicit social networked information. Existing approaches addressing TSCI lack satisfactory results due to the challenges related to data sparsity, accessibility and model efficiency. Motivated by the recent success of machine learning in trajectory mining, in this paper we formulate TSCI as a novel multi-label classification problem and develop a Recurrent Neural Network (RNN)-based framework called DeepTSCI to use human mobility patterns for inferring corresponding social circles. We propose three methods to learn the latent representations of trajectories, based on: (1) bidirectional Long Short-Term Memory (LSTM); (2) Autoencoder; and (3) Variational autoencoder. Experiments conducted on real-world datasets demonstrate that our proposed methods perform well and achieve significant improvement in terms of macro-R, macro-F1 and accuracy when compared to baselines.
- Apoorv Aggarwal, Sandip Ghoshal, Ankith M. S., Suhit Sinha, and Ganesh Ramakrishnan. 2017. Scalable Optimization of Multivariate Performance Measures in Multi-Instance Multi-label Learning. In AAAI.Google Scholar
- Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. 2006. Link prediction using supervised learning. In SDM.Google Scholar
- Basma Alharbi, Abdulhakim Ali Qahtan, and Xiangliang Zhang. 2016. Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces. In AAAI. Google ScholarDigital Library
- Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. 2016. Generating sentences from a continuous space. In CoNLL.Google Scholar
- Suthee Chaidaroon and Yi Fang. 2017. Variational Deep Semantic Hashing for Text Documents. In SIGIR. Google ScholarDigital Library
- Dawei Chen, Cheng Soon Ong, and Lexing Xie. 2016. Learning Points and Routes to Recommend Trajectories. In CIKM. Google ScholarDigital Library
- Zheqian Chen, Ben Gao, Huimin Zhang, Zhou Zhao, Haifeng Liu, and Deng Cai. 2017. User Personalized Satisfaction Prediction via Multiple Instance Deep Learning. In WWW. Google ScholarDigital Library
- Eunjoon Cho, Seth A Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In KDD. Google ScholarDigital Library
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).Google Scholar
- Andrew M Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In NIPS. Google ScholarDigital Library
- Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, and Eamonn J Keogh. 2008. Querying and mining of time series data - experimental comparison of representations and distance measures. In PVLDB. Google ScholarDigital Library
- Carl Doersch. 2016. Tutorial on Variational Autoencoders. arXiv (2016).Google Scholar
- Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, and Nitesh V. Chawla. 2014. Inferring user demographics and social strategies in mobile social networks. In KDD. Google ScholarDigital Library
- Ji Feng and Zhi-Hua Zhou. 2017. Deep MIML Network. In AAAI.Google Scholar
- Yarin Gal and Zoubin Ghahramani. 2015. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Statistics (2015), 285--290.Google Scholar
- Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xucheng Luo, and Fengli Zhang. 2017. Identifying Human Mobility via Trajectory Embeddings. In IJCAI. Google ScholarDigital Library
- Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, and Roberto Trasarti. 2011. Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J. 20, 5 (2011). Google ScholarDigital Library
- Fosca Giannotti, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. 2007. Trajectory pattern mining. In ACM SIGKDD. Google ScholarDigital Library
- Limin Guo, Guangyan Huang, Xu Gao, Jing He, Bin Wu, and Haoming Guo. 2015. DoSTra: discovering common behaviors of objects using the duration of staying on each location of trajectories. In AAAI Workshop.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Hsun-Ping Hsieh and Cheng-Te Li. 2014. Inferring Social Relationships from Mobile Sensor Data. In WWW Companion. Google ScholarDigital Library
- Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2017. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering. In IJCAI. Google ScholarDigital Library
- Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014. Semi-supervised learning with deep generative models. In NIPS. Google ScholarDigital Library
- Diederik P Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In ICLR.Google Scholar
- Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Recurrent Convolutional Neural Networks for Text Classification. In AAAI. Google ScholarDigital Library
- Xiaopeng Li and James She. 2017. Collaborative Variational Autoencoder for Recommender Systems.. In KDD. Google ScholarDigital Library
- Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2015. Personalized tour recommendation based on user interests and points of interest visit durations. In IJCAI.Google Scholar
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In KDD. Google ScholarDigital Library
- Hechen Liu and Markus Schneider. 2012. Similarity measurement of moving object trajectories. In SIGSPATIAL. Google ScholarDigital Library
- Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. In AAAI. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In ICLR.Google Scholar
- Ioannis Psorakis, Stephen J. Roberts, Iead Rezek, and Ben C. Sheldon. 2012. Inferring social network structure in ecological systems from spatio-temporal data streams. Journal of The Royal Society Interface 9, 76 (2012), 3055--3066.Google ScholarCross Ref
- Jesse Read and Fernando Perezcruz. 2014. Deep Learning for Multi-label Classification. Machine Learning 85, 3 (2014), 333--359. Google ScholarDigital Library
- Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. 2011. Classifier chains for multi-label classification. Machine Learning 85, 3 (2011), 333. Google ScholarDigital Library
- Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In ICML. Google ScholarDigital Library
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In NIPS. Google ScholarDigital Library
- Hongjian Wang, Zhenhui Li, and Wang-Chien Lee. 2014. PGT: Measuring mobility relationship using personal, global and temporal factors. In ICDM. Google ScholarDigital Library
- Yuhong Guo Xin Li. 2013. Active Learning with Multi-Label SVM Classification. In IJCAI.Google Scholar
- Weidi Xu, Haoze Sun, Chao Deng, and Ying Tan. 2017. Variational Autoencoder for Semi-Supervised Text Classification. In AAAI.Google Scholar
- Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, and Edward Y Chang. 2017. A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories. TOIS 35, 4 (2017), 36. Google ScholarDigital Library
- Dingqi Yang, Daqing Zhang, Longbiao Chen, and Bingqing Qu. 2015. Nation-Telescope: Monitoring and visualizing large-scale collective behavior in LBSNs. Journal of Network & Computer Applications 55 (2015), 170--180.Google ScholarCross Ref
- Guolei Yang and Andreas Züfle. 2017. Spatio-temporal Prediction of Social Connections. In Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data (GeoRich '17). ACM, New York, NY, USA, 6:1--6:6. Google ScholarDigital Library
- Chih Kuan Yeh, Wei Chieh Wu, Wei Jen Ko, and Yu Chiang Frank Wang. 2017. Learning Deep Latent Spaces for Multi-Label Classification. In AAAI.Google Scholar
- Josh Jia-Ching Ying, Wang-Chien Lee, and Vincent S. Tseng. 2013. Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM TIST 5, 1 (2013), 2:1--2:33. Google ScholarDigital Library
- Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wang-Chien Lee, Tz-Chiao Weng, and Vincent S Tseng. 2010. Mining user similarity from semantic trajectories. In SIGSPATIAL.Google Scholar
- Min-Ling Zhang and Zhi-Hua Zhou. 2007. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition 40, 7 (2007), 2038 -- 2048. Google ScholarDigital Library
- Shiquan Zhao, Jian Wu, Victor S. Sheng, Chen Ye, Pengpeng Zhao, and Zhiming Cui. 2015. Weak Labeled Multi-Label Active Learning for Image Classification. In MM. Google ScholarDigital Library
- Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, and Wei-Ying Ma. 2008. Understanding mobility based on GPS data. In UbiComp. Google ScholarDigital Library
- Chunting Zhou and Graham Neubig. 2017. Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction. In ACL.Google Scholar
- Zhi-Hua Zhou and Min-Ling Zhang. 2017. Multi-label Learning. Springer US, Boston, MA, 875--881.Google Scholar
- Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, and Yu-Feng Li. 2012. Multi-instance multi-label learning. Artificial Intelligence 176, 1 (2012), 2291 -- 2320. Google ScholarDigital Library
Index Terms
- Trajectory-based social circle inference
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