Routines - A System for Inference, Analysis and Prediction of Users Daily Location Visits: Industrial Paper

Inferring user behavior patterns in their daily location visits, i.e., where people go and how long they stay there, enables a variety of useful applications such as time management systems, new location recommendations, and the opportunity for analytics. For example, digital assistants can use inferred daily patterns to automate calendar events for users, or notify users about anticipated traffic conditions to their predicted next location. Retailers, on the other hand, can use the patterns to do location-based recommendations of venues similar or in proximity of the ones anticipated to be visited. To power the above applications we built and deployed Routines -a system for inferring periodic visits to known locations about users. Association rule mining has been demonstrated in the literature to be aptly suited for interpreting user routines and for building powerful audience understanding analytics tools. Using a large, real-world dataset of users visits, we perform a wide range of experiments showcasing the performance of our system for routines inference and prediction.

[1]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2001, Proceedings Eighth International Symposium on Temporal Representation and Reasoning. TIME 2001.

[2]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[3]  Xing Xie,et al.  Mining Individual Life Pattern Based on Location History , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[4]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[5]  Fei Wu,et al.  Where Did You Go: Personalized Annotation of Mobility Records , 2016, CIKM.

[6]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[7]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[8]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[9]  Wang-Chien Lee,et al.  Mining geographic-temporal-semantic patterns in trajectories for location prediction , 2013, ACM Trans. Intell. Syst. Technol..

[10]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[11]  Philip S. Yu,et al.  Mining asynchronous periodic patterns in time series data , 2000, KDD '00.

[12]  Nikos Mamoulis,et al.  Discovery of Periodic Patterns in Spatiotemporal Sequences , 2007, IEEE Transactions on Knowledge and Data Engineering.

[13]  Hui Xiong,et al.  Discovering colocation patterns from spatial data sets: a general approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[14]  Fang Dong,et al.  When and where next: individual mobility prediction , 2012, MobiGIS.