Analyzing Web behavior in indoor retail spaces

We analyze 18‐ million rows of Wi‐Fi access logs collected over a 1‐year period from over 120,000 anonymized users at an inner city shopping mall. The anonymized data set gathered from an opt‐in system provides users' approximate physical location as well as web browsing and some search history. Such data provide a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their web behavior, serving as a proxy to their information needs. We found that (a) there is a weekly periodicity in users' visits to the mall; (b) people tend to visit similar mall locations and web content during their repeated visits to the mall; (c) around 60% of registered Wi‐Fi users actively browse the web, and around 10% of them use Wi‐Fi for accessing web search engines; (d) people are likely to spend a relatively constant amount of time browsing the web while the duration of their visit may vary; (e) the physical spatial context has a small, but significant, influence on the web content that indoor users browse; and (f) accompanying users tend to access resources from the same web domains.

[1]  Jaime Teevan,et al.  Understanding the importance of location, time, and people in mobile local search behavior , 2011, Mobile HCI.

[2]  Djoerd Hiemstra,et al.  Query log analysis in the context of information retrieval for children , 2010, SIGIR '10.

[3]  Alton Yeow-Kuan Chua,et al.  Fulfilling mobile information needs: a study on the use of mobile phones , 2011, ICUIMC '11.

[4]  Torsten Suel,et al.  Analysis of geographic queries in a search engine log , 2008, LocWeb.

[5]  Arpita Khare,et al.  Influence of mall attributes and demographics on Indian consumers’ mall involvement behavior: An exploratory study , 2012 .

[6]  J. Rabianski,et al.  Shopping Center Appraisal and Analysis , 1993 .

[7]  Franziska Hoffmann,et al.  Spatial Tessellations Concepts And Applications Of Voronoi Diagrams , 2016 .

[8]  John Krogstie,et al.  Navigating MazeMap: Indoor human mobility, spatio-logical ties and future potential , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[9]  U J Rüetschi Wayfinding in scene space: modelling transfers in public transport , 2007 .

[10]  Yongli Ren,et al.  How People Use the Web in Large Indoor Spaces , 2014, CIKM.

[11]  Ryen W. White,et al.  From cookies to cooks: insights on dietary patterns via analysis of web usage logs , 2013, WWW.

[12]  Amanda Spink,et al.  From E-Sex to E-Commerce: Web Search Changes , 2002, Computer.

[13]  K. Evans,et al.  The impact of social influence and role expectations on shopping center patronage intentions , 1996 .

[14]  Yongli Ren,et al.  A new approach for indoor customer tracking based on a single Wi-Fi connection , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[15]  Ophir Frieder,et al.  Hourly analysis of a very large topically categorized web query log , 2004, SIGIR '04.

[16]  Amanda Spink,et al.  Vox populi: The public searching of the web , 2001, J. Assoc. Inf. Sci. Technol..

[17]  Virpi Roto,et al.  How people use the web on mobile devices , 2008, WWW.

[18]  Kai-Florian Richter,et al.  Hierarchical Representations of Indoor Spaces , 2011 .

[19]  Aisling Ann O'Kane,et al.  Contextual Influences on the Use and Non-Use of Digital Technology While Exercising at the Gym , 2015, CHI.

[20]  Archan Misra,et al.  LiveLabs: initial reflections on building a large-scale mobile behavioral experimentation testbed , 2013, MOCO.

[21]  Yang Song,et al.  Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance , 2013, WWW '13.

[22]  Barry Smyth,et al.  Understanding the intent behind mobile information needs , 2009, IUI.

[23]  Mark Sanderson,et al.  Analyzing geographic query reformulation: An exploratory study , 2014, J. Assoc. Inf. Sci. Technol..

[24]  Scott Bell,et al.  WiFi-based enhanced positioning systems: accuracy through mapping, calibration, and classification , 2010, ISA '10.

[25]  Shumeet Baluja,et al.  A large scale study of wireless search behavior: Google mobile search , 2006, CHI.

[26]  Christian S. Jensen,et al.  Indoor Positioning using Wi-Fi:How Well Is the Problem Understood? , 2013 .

[27]  Hua Lu,et al.  Indoor - A New Data Management Frontier , 2010, IEEE Data Eng. Bull..

[28]  Antonio Lima,et al.  Interdependence and predictability of human mobility and social interactions , 2012, Pervasive Mob. Comput..

[29]  Amanda Spink,et al.  An Analysis of Travel Information Searching on the Web , 2008, J. Inf. Technol. Tour..

[30]  Vigneshwaran Subbaraju,et al.  Accommodating user diversity for in-store shopping behavior recognition , 2014, SEMWEB.

[31]  Heather L. O'Brien,et al.  Exploring social context in mobile information behavior , 2014, ASIST.

[32]  Dietmar Wolfram,et al.  Search characteristics in different types of Web-based IR environments: Are they the same? , 2008, Inf. Process. Manag..

[33]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[34]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Nuria Oliver,et al.  Understanding mobile web and mobile search use in today's dynamic mobile landscape , 2011, Mobile HCI.

[36]  Amanda Spink,et al.  Searching the Web: the public and their queries , 2001 .

[37]  James D. Hollan,et al.  A diary study of mobile information needs , 2008, CHI.

[38]  Najafi Azadeh,et al.  REAL LIFE, REAL USERS AND REAL NEEDS: A STUDY AND ANALYSIS OF USER QUERIES ON THE WEB , 2008 .

[39]  M. Sanderson,et al.  Analyzing geographic queries , 2004 .

[40]  Heidi E. Julien,et al.  Information behavior , 2009, Annu. Rev. Inf. Sci. Technol..

[41]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[42]  Rita Wan-Chik,et al.  Investigating religious information searching through analysis of a search engine log , 2013, J. Assoc. Inf. Sci. Technol..

[43]  Chris Hodkinson,et al.  Consumer web search behaviour: diagrammatic illustration of wayfinding on the web , 2000, Int. J. Hum. Comput. Stud..

[44]  Barry Smyth,et al.  Mobile information access: A study of emerging search behavior on the mobile Internet , 2007, TWEB.

[45]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[46]  Ki-Joune Li,et al.  Similarity measures for trajectory of moving objects in cellular space , 2009, SAC '09.

[47]  Daniel Schulz,et al.  Human Mobility from GSM Data - A Valid Alternative to GPS? , 2012 .

[48]  Ravi Kumar,et al.  A characterization of online browsing behavior , 2010, WWW '10.

[49]  Stina Nylander,et al.  At home and with computer access: why and where people use cell phones to access the internet , 2009, CHI.

[50]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[51]  David M. Nichols,et al.  Contextual queries express mobile information needs , 2010, Mobile HCI.

[52]  Marcus Fontoura,et al.  Estimating advertisability of tail queries for sponsored search , 2010, SIGIR.

[53]  Christoph Hölscher,et al.  Taxonomy of Human Wayfinding Tasks: A Knowledge-Based Approach , 2009, Spatial Cogn. Comput..

[54]  Jinwoo Kim,et al.  Use Contexts for the Mobile Internet: A Longitudinal Study Monitoring Actual Use of Mobile Internet Services , 2005, Int. J. Hum. Comput. Interact..