Mining urban lifestyles: urban computing, human behavior and recommender systems

In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data [1,2], phone [3,4], and email communication [5] to Twitter activity [6] and opensource contributions on Wikipedia and OpenStreetMap [7,8]. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns [9]; call detail records (CDRs) present new opportunities to understand human mobility [10], analyze wealth [11], and model social network dynamics [12].

[1]  Taha Yasseri,et al.  Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis , 2011, PloS one.

[2]  Marijn ten Thij,et al.  Circadian Patterns in Twitter , 2014 .

[3]  Yong Yu,et al.  Inferring gas consumption and pollution emission of vehicles throughout a city , 2014, KDD.

[4]  Gabriel Cadamuro,et al.  Predicting poverty and wealth from mobile phone metadata , 2015, Science.

[5]  Xing Xie,et al.  Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data , 2016, CIKM.

[6]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[7]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[8]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2012, Journal of Statistical Physics.

[9]  Alex Pentland,et al.  Predicting Spending Behavior Using Socio-mobile Features , 2013, 2013 International Conference on Social Computing.

[10]  Xing Xie,et al.  Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach , 2010, AAAI.

[11]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[12]  Giovanni Quattrone,et al.  Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap , 2013, OpenSym.

[13]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[14]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[15]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[16]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[17]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[18]  Morse Steven,et al.  Persistent cascades: Measuring fundamental communication structure in social networks , 2016 .

[19]  Alex Pentland,et al.  The predictability of consumer visitation patterns , 2010, Scientific Reports.

[20]  Guillaume Bouchard,et al.  Group-sparse Embeddings in Collective Matrix Factorization , 2013, ICLR.

[21]  Kimmo Kaski,et al.  Circadian pattern and burstiness in mobile phone communication , 2011, 1101.0377.

[22]  Anup Basu,et al.  Graph regularized Lp smooth non-negative matrix factorization for data representation , 2019, IEEE/CAA Journal of Automatica Sinica.

[23]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[24]  Jari Saramäki,et al.  Daily Rhythms in Mobile Telephone Communication , 2015, PloS one.

[25]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[26]  Marta C. González,et al.  Sequences of purchases in credit card data reveal lifestyles in urban populations , 2017, Nature Communications.

[27]  Yu Zheng,et al.  Methodologies for Cross-Domain Data Fusion: An Overview , 2015, IEEE Transactions on Big Data.

[28]  W. Holben,et al.  Bacterial gene abundances as indicators of greenhouse gas emission in soils , 2010, The ISME Journal.

[29]  Pierre Vandergheynst,et al.  Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..