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How people use the web in large indoor spaces


Ren, Yongli; Tomko, Martin; Ong, Kevin; Sanderson, Mark (2014). How people use the web in large indoor spaces. In: CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai (China), 3 November 2014 - 7 November 2014, 1879-1882.

Abstract

We report a preliminary study of mobile Web behaviour in a large indoor retail space. By analysing a Web log collected over a 1 year period at an inner city shopping mall in Sydney, Australia, we found that 1) around 60% of registered Wi-Fi users actively browse the Internet, and the rest 40% do not, with around 10% of these users using Web search engines. Around 70% of this Web activity in the investigated mall come from frequent visitors; 2) the content that indoor users search for is different from the content they consume while browsing; 3) the popularity of future indoor search queries can be predicted with a simple theoretical model based on past queries treated as a weighted directed graph. The work described in this paper underpins applications such as the prediction of users’ information needs, retail recommendation systems, and improving the mobile Web search experience.

We report a preliminary study of mobile Web behaviour in a large indoor retail space. By analysing a Web log collected over a 1 year period at an inner city shopping mall in Sydney, Australia, we found that 1) around 60% of registered Wi-Fi users actively browse the Internet, and the rest 40% do not, with around 10% of these users using Web search engines. Around 70% of this Web activity in the investigated mall come from frequent visitors; 2) the content that indoor users search for is different from the content they consume while browsing; 3) the popularity of future indoor search queries can be predicted with a simple theoretical model based on past queries treated as a weighted directed graph. The work described in this paper underpins applications such as the prediction of users’ information needs, retail recommendation systems, and improving the mobile Web search experience.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:7 November 2014
Deposited On:25 Nov 2014 14:21
Last Modified:05 Apr 2016 18:32
Publisher:The ACM Guide to Computing Literature
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/2661829.2661929
Related URLs:http://dl.acm.org/results.cfm?h=1&cfid=602410799&cftoken=46798786
Permanent URL: https://doi.org/10.5167/uzh-101132

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