Rationality Analytics from Trajectories

The availability of trajectories tracking the geographical locations of people as a function of time offers an opportunity to study human behaviors. In this article, we study rationality from the perspective of user decision on visiting a point of interest (POI) which is represented as a trajectory. However, the analysis of rationality is challenged by a number of issues, for example, how to model a trajectory in terms of complex user decision processes? and how to detect hidden factors that have significant impact on the rational decision making? In this study, we propose Rationality Analysis Model (RAM) to analyze rationality from trajectories in terms of a set of impact factors. In order to automatically identify hidden factors, we propose a method, Collective Hidden Factor Retrieval (CHFR), which can also be generalized to parse multiple trajectories at the same time or parse individual trajectories of different time periods. Extensive experimental study is conducted on three large-scale real-life datasets (i.e., taxi trajectories, user shopping trajectories, and visiting trajectories in a theme park). The results show that the proposed methods are efficient, effective, and scalable. We also deploy a system in a large theme park to conduct a field study. Interesting findings and user feedback of the field study are provided to support other applications in user behavior mining and analysis, such as business intelligence and user management for marketing purposes.

[1]  Christos Faloutsos,et al.  Fast mining and forecasting of complex time-stamped events , 2012, KDD.

[2]  Moshe Tennenholtz,et al.  Rational Competitive Analysis , 2001, IJCAI.

[3]  Philip S. Yu,et al.  Transferring heterogeneous links across location-based social networks , 2014, WSDM.

[4]  Siyuan Liu,et al.  Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise , 2014, AAAI.

[5]  S. Alpern,et al.  Spatial Dispersion as a Dynamic Coordination Problem , 2002 .

[6]  Xiao Liang,et al.  Where to wait for a taxi? , 2012, UrbComp '12.

[7]  Huan Liu,et al.  Unsupervised feature selection for linked social media data , 2012, KDD.

[8]  Stuart J. Russell.  Rationality and Intelligence , 1995, IJCAI.

[9]  Prasanna Velagapudi,et al.  Distributed model shaping for scaling to decentralized POMDPs with hundreds of agents , 2011, AAMAS.

[10]  Ramayya Krishnan,et al.  A quantitative analysis of decision process in social groups using human trajectories , 2014, AAMAS.

[11]  Ramayya Krishnan,et al.  Adaptive collective routing using gaussian process dynamic congestion models , 2013, KDD.

[12]  Stuart J. Russell,et al.  The BATmobile: Towards a Bayesian Automated Taxi , 1995, IJCAI.

[13]  Jiawei Han,et al.  Towards Active Learning on Graphs: An Error Bound Minimization Approach , 2012, 2012 IEEE 12th International Conference on Data Mining.

[14]  Thomas J. Walsh,et al.  Knows what it knows: a framework for self-aware learning , 2008, ICML '08.

[15]  Ramayya Krishnan,et al.  Understanding Sequential Decisions via Inverse Reinforcement Learning , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[16]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[17]  Scott C. Linn,et al.  Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning , 2007, Manag. Sci..

[18]  Ashok N. Srivastava,et al.  Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study , 2010, KDD.

[19]  Nitesh V. Chawla,et al.  Predicting Links in Multi-relational and Heterogeneous Networks , 2012, 2012 IEEE 12th International Conference on Data Mining.

[20]  Thomas M. Stoker,et al.  Semiparametric Estimation of Index Coefficients , 1989 .

[21]  Andrea Cavallaro,et al.  Multifeature Object Trajectory Clustering for Video Analysis , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Jing Yuan,et al.  On Discovery of Traveling Companions from Streaming Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[23]  Hao Wang,et al.  Location recommendation in location-based social networks using user check-in data , 2013, SIGSPATIAL/GIS.

[24]  Jesse Davis,et al.  Markov Network Structure Learning: A Randomized Feature Generation Approach , 2012, AAAI.

[25]  Philip S. Yu,et al.  Meta-path based multi-network collective link prediction , 2014, KDD.

[26]  Favyen Bastani,et al.  Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps , 2011, SSTD.

[27]  Eric P. Xing,et al.  Grafting-light: fast, incremental feature selection and structure learning of Markov random fields , 2010, KDD '10.

[28]  Nicholas Jing Yuan,et al.  T-Finder: A Recommender System for Finding Passengers and Vacant Taxis , 2013, IEEE Transactions on Knowledge and Data Engineering.

[29]  William D. Smart,et al.  A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation , 2010, UAI.

[30]  A. Juditsky,et al.  Direct estimation of the index coefficient in a single-index model , 2001 .

[31]  Archan Misra,et al.  TODMIS: mining communities from trajectories , 2013, CIKM.

[32]  Milind Tambe,et al.  Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping , 2009, ICAPS.

[33]  Jiawei Han,et al.  A Variance Minimization Criterion to Active Learning on Graphs , 2012, AISTATS.

[34]  Jiawei Han,et al.  ACM Transactions on Knowledge Discovery from Data: Introduction , 2007 .

[35]  Christos Faloutsos,et al.  Fast and reliable anomaly detection in categorical data , 2012, CIKM.

[36]  Kenji Yamanishi,et al.  Mining abnormal patterns from heterogeneous time‐series with irrelevant features for fault event detection , 2009, Stat. Anal. Data Min..