Kevin Ong

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

This paper investigates if Information Foraging Theory can be used to understand differences in user behavior when searching on mobile and desktop web search systems. Two groups of thirty-six participants were recruited to carry out six identical web search tasks on desktop or on mobile. The search tasks were prepared with a different number and distribution of relevant documents on the first result page. Search behaviors on mobile and desktop were measurably different. Desktop participants viewed and clicked on more results but saved fewer as relevant, compared to mobile participants, when information scent level increased. Mobile participants achieved higher search accuracy than desktop participants for tasks with increasing numbers of relevant search results. Conversely, desktop participants were more accurate than mobile participants for tasks with an equal number of relevant results that were more distributed across the results page. Overall, both an increased number and better positioning of relevant search results improved the ability of participants to locate relevant results on both desktop and mobile. Participants spent more time and issued more queries on desktop, but abandoned less and saved more results for initial queries on mobile.

The aim of this research proposal is to understand the extent in which Information Scent, part of Information Foraging Theory, influence the perception of relevance of retrieved search engine result pages (SERPs) in different environments (desktop and mobile). %We intend to study this influence by observing information seekers' behavior when they are searching for topics on a typical search engine. Firstly, we planned to investigate how previously found correlations between information scent and search behavior on desktop SERPs may also applicable on mobile. Secondly, we investigate if the differences in search behavior are also caused by SERP visibility and/or physical input behavior in different environments. Lastly, we would like to observe the extent in which changing snippet length can affect web search behavior. The perception of relevance on SERPs might be similar in both environments because the arrangement of SERPs on both mobile and desktop are identical; or different because the visibility of search results is less on mobile compared to desktop, due to limited screen sizes. We wanted to study the extent of which the physical limitations might significantly impact search behavior in different environments due to expected ``hidden scent". We conducted two user studies to observe these phenomenon and seek to understand search behavior by manipulating the SERPs presented to users. We observed both similarities and differences in web search behavior between the environments. On the desktop, increasing information scent led to lower positions of search results saved as relevant and lower positions of mouse hover. However, on mobile, increasing relevant search results beyond the initial screen size reduced number of documents examined.

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.

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.

Wi-Fi and smartphone based location tracking technologies have been widely applied in location based services (LBS). This research is a part of an Australian Research Council (ARC) project required by a large global shopping mall company located in Australia. It aims to develop an effective customer tracking approach for acquiring shopping behavior of customers and providing them better services. Currently, the log data provided by the company only recorded one Wi-Fi connection at a time for one smartphone user. This makes most of the conventional tracking and positioning methods inapplicable. In order to solve this problem, a new approach was developed, which takes advantage of both the cell of origin (CoO) method and the fingerprinting method. Its implementation mainly includes two phases: 1) the cell determination phase, including partitioning of the floors of the shopping mall using a Voronoi diagram and determining a unique cell for each wireless access point (AP), adjusting the cells manually according to the real floor layout, obstructions as well as an on-site observing and calibrating process. An AP-shop/room matching table and other relevant information are stored in a database; and 2) the user location tracking phase, including determination of the cell that the customer locates in according to the detected AP linked with the customer's mobile phone. A back-tracing method is also used to improve the tracking performance. This new approach was tested in a large shopping-mall-like space covering an area of 1000 square meters, containing 8 simulated shops and 11 APs. The results showed a 96% correct detection rate whilst the 4% erroneous detections occurred when a user was near the cell boundaries. This new approach is also easy to implement and integrate with other location tracking systems.