Manpreet Kaur
Mark Sanderson
ABSTRACT
This paper investigates the Cyber-Physical behavior of a user in a large indoor shopping center by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the center operators. Our analysis shows that many users exhibit high correlation between their cyber activities and physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from Wikipedia concepts and compute a contextual similarity that represents a customer's activities with the mall context. We further show the use of cyber-physical contextual similarity in two different applications: user behavior classification and future location prediction. The experimental results demonstrate that the users' contextual similarity significantly improves the accuracy of such applications.
- Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. Dbpedia: A nucleus for a web of open data. Springer.Google Scholar
- Y. B. Bai, S. Wu, G. Retscher, A. Kealy, L. Holden, M. Tomko, A. Borriak, B. Hu, M. Sanderson, H. R. Wu, and K. Zhang. 2014. A New Method for Improving Wi-Fi Based In-door Positioning Accuracy. Springer Verlag, Berlin.Google Scholar
- Karen Church and Barry Smyth. 2009. Understanding the Intent Behind Mobile Information Needs. In IUI (09). ACM, New York, NY, USA, 247--256. Google ScholarDigital Library
- Milan Dojchinovski and Tomas Kliegr. 2013. Entityclassifier.eu: Real-time Classification of Entities in Text with Wikipedia. In ECMLPKDD (2013). 1--1. Google ScholarDigital Library
- Huizhong Duan and ChengXiang Zhai. 2015. Mining Coordinated Intent Representation for Entity Search and Recommendation. In CIKM (2015). ACM, New York, NY, USA, 333--342. Google ScholarDigital Library
- Moustafa Elhamshary and Moustafa Youssef. 2014. CheckInside: A Fine-grained Indoor Location-based Social Network. In UbiComp (14). ACM, New York, NY, USA, 607--618. Google ScholarDigital Library
- Mark A Hall and Eibe Frank. 2008. Combining Naive Bayes and Decision Tables.. In FLAIRS Conference, Vol. 2118. 318--319.Google Scholar
- Henry Hsu and Peter A Lachenbruch. 2008. Paired t test. Wiley Encyclopedia of Clinical Trials (2008).Google Scholar
- Bernard J. Jansen, Danielle L. Booth, and Amanda Spink. 2008. Determining the informational, navigational, and transactional intent of Web queries. Information Processing & Management 44, 3 (May 2008), 1251--1266. Google ScholarDigital Library
- Kasthuri Jayarajah, Youngki Lee, Archan Misra, and Rajesh Krishna Balan. 2015. Need Accurate User Behaviour?: Pay Attention to Groups!. In UbiComp (15). ACM, New York, NY, USA, 855--866. Google ScholarDigital Library
- John Krumm and Dany Rouhana. 2013. Placer: Semantic Place Labels from Diary Data. In UbiComp (13). ACM, New York, NY, USA, 163--172. Google ScholarDigital Library
- Michal Laclavík, Marek Ciglan, Sam Steingold, Martin Seleng, Alex Dorman, and Stefan Dlugolinsky. 2015. Search Query Categorization at Scale. In WWW. International World Wide Web Conferences Steering Committee, 1281--1286. Google ScholarDigital Library
- Claudio Martella, Armando Miraglia, Marco Cattani, and Maarten van Steen. 2016. Leveraging Proximity Sensing to Mine the Behavior of Museum Visitors. In PerCom 2016. IEEE.Google Scholar
- Archan Misra and Rajesh Krishna Balan. 2013. LiveLabs: Initial Reflections on Building a Large-scale Mobile Behavioral Experimentation Testbed. SIGMOBILE Mob. Comput. Commun. Rev. 17, 4 (Dec. 2013), 47--59. Google ScholarDigital Library
- Wendy W Moe. 2003. Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of consumer psychology 13, 1 (2003), 29--39.Google ScholarCross Ref
- Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. 2012. Mining user mobility features for next place prediction in location-based services. In ICDM. IEEE, 1038--1043. Google ScholarDigital Library
- Veljko Pejovic, Neal Lathia, Cecilia Mascolo, and Mirco Musolesi. 2015. Mobile-Based Experience Sampling for Behaviour Research. arXiv preprint arXiv:1508.03725 (2015).Google Scholar
- Meeralakshmi Radhakrishnan, Sharanya Eswaran, Archan Misra, Deepthi Chander, and Koustuv Dasgupta. 2016. IRIS: Tapping Wearable Sensing to Capture In-Store Retail Insights on Shoppers. In PerCom 2016. IEEE.Google Scholar
- Yongli Ren, Gang Li, and Wanlei Zhou. 2015. A survey of recommendation techniques based on offline data processing. Concurrency and Computation: Practice and Experience 27, 15 (2015), 3915--3942. Google ScholarDigital Library
- Y. Ren, M. Tomko, F. D. Salim, J. Chan, C. L. A. Clarke, and M. Sanderson. 2018. A Location-Query-Browse Graph for Contextual Recommendation. IEEE TKDE 30, 2 (Feb 2018), 204--218.Google Scholar
- Yongli Ren, Martin Tomko, Flora D. Salim, Jeffrey Chan, and Mark Sanderson. 2018. Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces. EPJ Data Science 7, 1 (2018), 1.Google ScholarCross Ref
- Yongli Ren, Martin Tomko, Flora Dilys Salim, Kevin Ong, and Mark Sanderson. 2017. Analyzing Web behavior in indoor retail spaces. JASIST 68, 1 (2017), 62--76. Google ScholarDigital Library
- Gerard Salton and Christopher Buckley. 1988. Term-weighting Approaches in Automatic Text Retrieval. Inf. Process. Manage. 24, 5 (Aug. 1988), 513--523. Google ScholarDigital Library
- C. W. You, H. L. C. Kao, B. J. Ho, Y. H. T. Chen, W. F. Wang, L. T. Bei, H. H. Chu, and M. S. Chen. 2014. ConvenienceProbe: A Phone-Based System for Retail Trade-Area Analysis. IEEE Pervasive Computing 13, 1 (Jan. 2014), 64--71. Google ScholarDigital Library
- C. W. You, C. C. Wei, Y. L. Chen, H. h Chu, and M. S. Chen. 2011. Using Mobile Phones to Monitor Shopping Time at Physical Stores. IEEE Pervasive Computing 10, 2 (April 2011), 37--43. Google ScholarDigital Library
- Yunze Zeng, Parth H. Pathak, and Prasant Mohapatra. 2015. Analyzing Shopper's Behavior Through WiFi Signals. In Proceedings of the 2Nd Workshop on Workshop on Physical Analytics (WPA '15). ACM, New York, NY, USA, 13--18. Google ScholarDigital Library
Index Terms
- Shopping intent recognition and location prediction from cyber-physical activities via wi-fi logs
Recommendations
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
This article investigates the cyber-physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high ...
Cyber Physical Socio Ecology
Exploring the laws of the nature and the rules of human society is the grand challenge of sciences. The Internet, Web, various communication networks and digital devices are connecting each other to form an enormous cyber space. The cyber space ...
Comments