Visual Analysis of User Behavior From Web Log Data

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Visual Analysis of User Behavior From Web Log Data"

By

Mr. Conglei SHI


Abstract

With the decreasing cost and the increasing storage capacity, more web log 
data can be recorded nowadays. Compared with the data collected from 
experiments, the log data can more accurately reflect the user behavior 
with little bias. These data provide an opportunity to understand user 
behavior and help improve user experience. For example, by analyzing how 
users use search engines and why they are not satisfied with the search 
result, we can improve the usability of search engines, such as search 
personalization and search accuracy. However, analyzing the log data is 
challenging. For instance, exploring the raw data is an essential step to 
formulate hypotheses and build models, but the log data size is large and 
increases over time. Visual analytic methods, in the case, can greatly 
help explore and analyze the data, because using visualization components 
enables to express a large amount of information in a very efficient and 
intuitive way, since human perceptual system can process visual 
information rapidly, and it can help start analysis without assumptions.

In this thesis, we focus on two types of log data. The first one is the 
search log data, which record how users use different search engines to 
perform queries and is collected from a world wide distributed web 
browser. The second one is the learning log data from a Massive Open 
Online Courses (MOOCs) platform. In order to better understand the actual 
needs when analyzing the log data, we conducted several rounds of 
interviews with domain experts who are the end users of visual analytics 
systems. After that, we follow the user centered design and iteratively 
design three analytics systems. In the first system, RankExplorer, we 
present a new visualization technique to intuitively show the ranking 
changes of queries in search log data. In the second system, LoyalTracker, 
we target on better understanding user loyalty and defection behavior in 
search log data. In the third system, VisMOOC, we focus on analyzing 
learning behavior through learning log data. All the three systems give 
domain experts new insights into user behavior. In order to validate the 
effectiveness and usefulness of proposed systems, we conducted case 
studies with domain experts and one user study for RankExplorer.


Date:			Monday, 25 August 2014

Time:			2:00pm - 4:00pm

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Ajay Joneja (IELM)

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Prof. Pedro Sander
 			Prof. Chiew-Lan Tai
 			Prof. Ravindra GOONETILLEKE (IELM)
                        Prof. Kwan-Liu Ma (Comp. Sci., Univ. of Calif-Davis)


**** ALL are Welcome ****